Ocular Artifacts in EEG Analysis: Impacts, Correction Methods, and Best Practices for Biomedical Research

Abstract

Ocular artifacts, including blinks and saccades, pose a significant challenge in electroencephalographic (EEG) data analysis by introducing large-amplitude, low-frequency signals that can obscure crucial neural information and lead to data misinterpretation. This article provides a comprehensive overview for researchers and drug development professionals, covering the foundational physiology of these artifacts, their specific impacts on signal integrity, and a detailed evaluation of both established and emerging correction methodologies. We further offer a practical guide for troubleshooting and optimizing artifact handling in diverse experimental setups, from traditional lab-based systems to modern wearable EEG, and conclude with a comparative analysis of validation metrics to inform robust analytical pipelines in clinical and translational neuroscience.

Understanding Ocular Artifacts: Physiological Origins and Their Impact on EEG Signal Integrity

The human eye is not merely a passive sensory organ but an active source of significant electrophysiological phenomena that profoundly impact electroencephalographic (EEG) research. Three primary physiological sources—the corneo-retinal dipole, eyelid movements, and extraocular muscle activity—generate electrical potentials that can contaminate EEG recordings, presenting substantial challenges for neuroscientists and clinical researchers. These ocular artifacts exhibit amplitudes that often dwarf genuine neural signals, with their frequency bandwidth (3–15 Hz) critically overlapping with diagnostically important brain rhythms such as theta and alpha waves [1]. Understanding the precise mechanisms through which these ocular structures generate artifacts is fundamental to developing effective correction methodologies, ensuring the integrity of neural data, and advancing both basic research and applied clinical studies, including drug development projects investigating neurophysiological outcomes.

Physiological Foundations of Ocular Artifacts

The Corneo-Retinal Dipole (CRD)

The corneo-retinal dipole represents a fundamental bioelectrical phenomenon central to understanding ocular artifacts in EEG. This dipole arises from the transmembrane potential differences between the positively charged cornea and the negatively charged retina, creating a stable electrical field that spans the eyeball [2] [1]. This potential difference, measuring approximately 6-10 mV in the resting state, transforms the entire eyeball into a biological electric dipole [3]. During any rotational movement of the eyeball, this dipole field rotates correspondingly within the conductive medium of the head. This movement generates widespread potential changes across the scalp that are detectable by EEG electrodes, with the frontal regions being most significantly affected due to their proximity to the ocular globes. Research has demonstrated that the CRD-related artifacts can be considered stationary for at least 1-1.5 hours, validating the feasibility of calibration-based correction approaches for both offline and online EEG analysis [2].

Eyelid Anatomy and Movement Artifacts

The eyelids are complex, multi-layered structures whose movements introduce substantial artifacts distinct from those generated by the CRD. Anatomically, the eyelids consist of three primary lamellae:

• Anterior Lamella: Comprising the thin skin (approximately 1 mm thick, among the thinnest in the human body) and the orbicularis oculi muscle, a concentric striated muscle responsible for eyelid closure [4].
• Middle Lamella: Containing the orbital septum (a fibrous membrane) and fat pads that provide structural separation [4].
• Posterior Lamella: Housing the tarsal plate (dense connective tissue providing mechanical support), retractors (levator palpebrae superioris and Müller's muscle), and the palpebral conjunctiva (a mucous membrane) [4].

The primary eyelid movements are controlled by two key muscular systems: the levator palpebrae superioris (innervated by CN III) for eyelid elevation, and the orbicularis oculi (innervated by CN VII) for eyelid closure [5] [6]. During blinking, the rapid movement of the eyelid across the corneal surface introduces high-amplitude potential field changes independent of eyeball rotation [2] [1]. The eyelid itself acts as a sliding conductive layer that modulates the electrical field generated by the underlying CRD, creating characteristic spike-like artifacts in the EEG signal that are particularly prominent in frontal electrodes.

Extraocular Muscles and Electromyogenic Artifacts

The extraocular muscles (EOMs) represent a specialized group of seven skeletal muscles responsible for controlling eyeball movement and eyelid elevation. These include the four rectus muscles (superior, inferior, medial, and lateral), two oblique muscles (superior and inferior), and the levator palpebrae superioris [7] [8]. These muscles exhibit a significantly lower nerve-to-muscle fiber ratio (1:3 to 1:5) compared to other skeletal muscles (1:50 to 1:125), enabling precise control but also generating substantial electrical activity during contraction [7].

Innervation is provided by three cranial nerves: the oculomotor nerve (CN III) supplies the majority of EOMs, the trochlear nerve (CN IV) innervates the superior oblique, and the abducens nerve (CN VI) controls the lateral rectus [7] [8]. Contractions of these muscles during saccades, smooth pursuit, and fixation generate electromyographic (EMG) signals that manifest as high-frequency bursts in the EEG, typically in the 30-100 Hz range, though their harmonics can affect lower frequencies crucial for brain rhythm analysis [1].

Table 1: Extraocular Muscles and Their Functions

Quantitative Characterization of Ocular Artifacts

Ocular artifacts exhibit distinct electrophysiological properties that enable their identification and quantification in EEG signals. The characteristics vary significantly between the different physiological sources, necessitating tailored correction approaches for each artifact type.

Table 2: Quantitative Characteristics of Ocular Artifacts in EEG

Methodologies for Experimental Investigation

Calibration Protocols for Artifact Characterization

Establishing reliable experimental protocols is essential for systematic investigation of ocular artifacts. The sparse generalized eye artifact subspace subtraction (SGEYESUB) algorithm, which demonstrates state-of-the-art correction performance, utilizes a calibration data acquisition protocol requiring approximately five minutes per subject [2]. This protocol involves:

• Participant Preparation: Application of high-density EEG electrodes (typically 64+ channels) with simultaneous EOG recording to monitor ocular activity.
• Calibration Task: Participants perform a structured sequence of ocular movements including:
  • Voluntary blinks (10-15 repetitions at 3-second intervals)
  • Horizontal saccades (following a visual target between 10° left and right positions)
  • Vertical saccades (following a target between 10° up and down positions)
  • Smooth pursuit (tracking a slowly moving visual target in circular patterns)
• Data Acquisition: EEG is recorded at a minimum sampling rate of 500 Hz to adequately capture both slow (CRD) and fast (EMG) components, with trigger markers indicating movement onset.

This calibration data enables the construction of subject-specific artifact templates that account for individual anatomical variations in skull conductivity, eye socket geometry, and dipole strength [2].

Histological Examination Techniques

For fundamental research into ocular artifact mechanisms, histological analysis provides structural insights. Recent investigation of the radar/ultrasound analogy for retinal function employed the following methodology in a rabbit model [3]:

• Tissue Preparation: Bilateral retinal tissues were carefully excised and horizontally embedded in paraffin blocks to optimize visualization of all retinal layers.
• Staining Protocols: Sections were stained using:
  • Hematoxylin and Eosin (H&E) for general morphological analysis
  • Masson's Trichrome (MTC) for connective tissue differentiation
• Neuronal Density Assessment: Employing the physical dissector method, a stereological technique that provides unbiased estimates of particle number without assumptions about particle shape, size, or orientation [3].
• Layer-by-Layer Analysis: The ten distinct retinal layers were examined for structural analogies to radar/ultrasound system components, with the outermost layer compared to an acoustic lens and the ganglion cell layer analyzed for piezoelectric transducer-like properties [3].

Advanced Signal Processing and Correction Methodologies

Algorithmic Approaches for Artifact Correction

Multiple computational approaches have been developed to address the challenge of ocular artifacts in EEG data, each with distinct strengths and applications:

• Regression-Based Methods: These traditional approaches operate under the linearity assumption that the recorded EEG signal represents the cumulative sum of neural activity and artifacts: RawEEG(n) = EEG(n) + artifacts(n) [1]. They utilize electrooculographic (EOG) channels or frontal EEG electrodes as ocular artifact templates to estimate channel-specific weighting coefficients (β) that quantify artifact influence, which is then subtracted from the contaminated signal [1].

• Independent Component Analysis (ICA): This blind source separation technique decomposes multichannel EEG data into statistically independent components, enabling identification and removal of ocular artifact-related components [1]. ICA is particularly effective with high-density EEG systems (40+ channels) and can successfully separate neural activity from both CRD and eyelid movement artifacts.

• Sparse Generalized Eye Artifact Subspace Subtraction (SGEYESUB): This advanced algorithm offers state-of-the-art correction performance by maximizing preservation of resting brain activity and event-related potentials while reducing residual correlations between corrected EEG channels and eye artifacts to below 0.1 [2]. Once fitted to calibration data (~5 minutes), the correction reduces to a simple matrix multiplication, enabling both offline and real-time application.

• Artifact Subspace Reconstruction (ASR): This adaptive method operates by detecting and reconstructing the subspace of EEG data contaminated by artifacts using statistical properties of the signal [1]. ASR is particularly valuable for continuous EEG recordings and real-time applications like brain-computer interfaces.

• Deep Learning-Based Approaches: Emerging methodologies employ deep neural networks trained on clean EEG signals to recognize and correct non-physiological patterns [1]. These show promise for handling complex, non-stationary artifacts but require substantial training data.

Impact on Multivariate Pattern Analysis

The influence of artifact correction extends to advanced analytical approaches like multivariate pattern analysis (MVPA). Recent research examining support vector machines (SVM) and linear discriminant analysis (LDA) for EEG decoding reveals that the combination of artifact correction and rejection generally does not improve decoding performance in most cases across seven common event-related potential paradigms (N170, mismatch negativity, N2pc, P3b, N400, lateralized readiness potential, and error-related negativity) [9]. However, artifact correction remains essential to minimize artifact-related confounds that might artificially inflate decoding accuracy, potentially leading to incorrect conclusions about neural representations [9].

The Scientist's Toolkit: Essential Research Reagents and Materials

Table 3: Essential Research Materials for Ocular Artifact Investigation

The comprehensive understanding of corneo-retinal dipole physiology, eyelid movement dynamics, and extraocular muscle function provides the essential foundation for addressing one of the most persistent challenges in EEG research. The systematic characterization of these artifact sources enables the development of increasingly sophisticated correction methodologies that preserve neural signals while removing non-cerebral contaminants. As EEG technology expands into wearable devices and real-time brain-computer interfaces, the demand for robust, efficient artifact handling continues to grow. Future research directions include refining real-time correction algorithms, exploring novel biomedical sensing modalities for improved artifact detection, and developing standardized validation frameworks for artifact correction performance across diverse populations and recording conditions. For drug development professionals and clinical researchers, maintaining rigorous standards for ocular artifact management remains paramount for ensuring the validity and interpretability of electrophysiological biomarkers in both basic research and therapeutic applications.

Ocular artifacts represent one of the most significant sources of contamination in electroencephalography (EEG) data, posing substantial challenges for neuroscientific research and clinical applications. These electrical potentials generated by eye movements and blinks can overwhelm genuine neural signals, leading to misinterpretation of brain activity. Within the context of a broader thesis on how ocular artifacts affect EEG data analysis research, this technical guide provides a comprehensive characterization of three primary ocular artifact types: blinks, saccades, and microsaccades. Understanding the origin, properties, and impact of these artifacts is fundamental to developing effective correction methodologies and ensuring the validity of EEG findings in both basic research and drug development applications.

Physiological Origins and Characteristics

Ocular artifacts originate from the electrical field created by the corneo-retinal dipole, where the cornea carries a positive charge relative to the negatively charged retina [10]. When the eye moves or blinks, this dipole field shifts position relative to EEG electrodes, producing measurable electrical potentials that contam neural recordings.

Characterization of Blinks

Eye blinks are characterized by very high amplitude negative waveforms in the bifrontal regions [10]. The underlying mechanism involves Bell's Phenomenon, where the eyes roll upward during a blink, bringing the positive cornea closer to the frontal electrodes Fp1 and Fp2 [10]. This movement produces a positive signal deflection that is most prominent in frontal leads without significant spread to posterior regions. Blinks are a normal component of awake EEG and typically last 100-400 milliseconds [11]. Unlike cerebral signals, blinks lack a posterior field, have no preceding spike before the larger amplitude wave, and cause minimal disruption to the background neural activity [10].

Characterization of Saccades

Saccades are rapid, conjugate eye movements used to reorient the foveal region to new spatial locations, occurring approximately 3 times per second [12]. These "ballistic" movements are characterized by high velocity and brief duration, during which visual processing is largely suppressed. In EEG recordings, lateral saccades produce opposing polarities in F7 and F8 electrodes due to the corneo-retinal dipole [10]. When looking to the right, the right cornea approaches F8 (creating a positive charge) while the left retina moves toward F7 (creating a negative charge), resulting in a characteristic "phase reversal" pattern [10]. The saccadic spike artifact (SP) at saccade onset is particularly problematic as it can resemble synchronous neuronal gamma band activity [13].

Characterization of Microsaccades

Microsaccades are very small, involuntary eye movements (typically <1.0°) that occur during attempted visual fixation at an average rate of 1-2 per second [14] [12]. These tiny flicks are embedded within slower drifting movements and represent the most prominent contribution to fixational eye movements. Despite their small size, microsaccades generate significant artifacts through two primary mechanisms: extraocular muscle activity that propagates to the EEG as a saccadic spike potential, and genuine cortical activity manifested in the EEG 100-140 ms after movement onset [14]. This cortical response resembles the visual lambda response evoked by larger voluntary saccades, challenging the standard assumption that brain activity from saccades is precluded during fixation [14].

Table 1: Comparative Characteristics of Ocular Artifacts

Impact on EEG Data Analysis

Signal Contamination Mechanisms

Ocular artifacts compromise EEG data through multiple mechanisms. Blink artifacts primarily contaminate the low-frequency EEG bands (0-12 Hz) that are associated with critical cognitive processes including hand movements, attention levels, and drowsiness [11]. The high amplitude of blink artifacts can saturate amplifier inputs, causing transient signal loss and distorting event-related potentials. Saccadic movements generate spike potentials that manifest as broadband artifacts in the EEG spectrum, particularly problematic in the gamma frequency range where they can mimic induced gamma band activity [13]. Microsaccades present a more insidious challenge because they occur frequently during fixation tasks and generate both myogenic artifacts from extraocular muscles and genuine cortical responses that are difficult to disentangle from stimulus-related activity [14].

Consequences for Research Interpretation

The presence of ocular artifacts can lead to systematic biases in EEG analysis. In event-related potential studies, blink artifacts time-locked to stimuli can distort component amplitudes and latencies, particularly for frontal components. For frequency domain analyses, saccade-related spike potentials can artificially inflate power estimates in gamma band ranges, potentially leading to false conclusions about neural synchronization [13]. Microsaccades introduce additional confounding factors because their probability often varies systematically with experimental conditions; for example, microsaccade probability modulates according to the proportion of target stimuli in oddball tasks, causing artifactual modulations of late stimulus-locked ERP components [14].

Table 2: Research Reagent Solutions for Ocular Artifact Management

Experimental Protocols for Characterization

Protocol for Blink Artifact Investigation

The characterization of blink artifacts and their relationship to perceptual processes requires carefully controlled paradigms. In one experimental design, participants view ambiguous plaid stimuli while continuously reporting their perceptual experience [15]. The stimulus consists of moving gratings superimposed over each other, creating bistable perception where viewers alternate between seeing unidirectional coherent motion or bidirectional component movement [15]. During testing, participants are seated in a dark room 40 cm from the display with heads stabilized using a chin rest. Binocular eye movements are recorded at 120 Hz using eye tracking glasses synchronized with EEG acquisition [15]. Participants provide continuous perceptual reports via response buttons, with button lifts indicating perceptual switches. This protocol enables precise correlation between blink events, neural activity, and perceptual changes.

Protocol for Microsaccade Detection and Analysis

High-quality recording of microsaccades requires specialized equipment and analytical approaches. In a typical fixation paradigm, participants maintain gaze on a central fixation point during 10-second trials while being instructed to avoid blinks and large eye movements [14]. Stimuli may include checkerboard patterns or face images presented on a monitor. Eye movements are recorded monocularly from one eye using infrared video-based eye trackers with high sampling rates (500 Hz or greater) and high spatial resolution (0.01° or better) [14]. Microsaccades are detected using velocity-based algorithms that identify outliers in two-dimensional velocity space [14]. The critical parameters include a velocity threshold set to 5 median-based standard deviations of the velocity values, minimum duration of 6 ms, maximum magnitude of 1°, and minimum temporal separation of 50 ms from previous microsaccades [14]. EEG segments are then extracted around microsaccade onset and baseline-corrected for analysis.

Diagram 1: Experimental Workflow for Ocular Artifact Characterization

Detection and Removal Methodologies

Traditional and Machine Learning Approaches

Multiple computational approaches have been developed to identify and remove ocular artifacts from EEG signals. For single-channel EEG, a combined k-means and Singular Spectrum Analysis (SSA) method has demonstrated efficacy by extracting eye-blink artifacts based on time-domain features without modifying uncontaminated regions of the EEG signal [11]. This approach involves mapping the single-channel EEG signal into a multivariate data matrix, computing time-domain features (energy, Hjorth mobility, kurtosis, and range), applying k-means clustering to identify artifact components, processing these with SSA, and finally subtracting the estimated artifact from the contaminated signal [11].

Comparative studies of machine learning classifiers have identified Artificial Neural Networks (ANN) as particularly effective when combined with scalp topography features for eye-blink artifact detection [17]. Other classifiers including Support Vector Machines (SVM) and Linear Discriminant Analysis (LDA) have shown varying levels of performance across different feature sets [18] [17]. Importantly, research indicates that the combination of artifact correction and rejection does not necessarily enhance decoding performance in multivariate pattern analysis, though artifact correction remains essential to minimize confounds that might artificially inflate decoding accuracy [18].

Validation Frameworks

Robust validation of artifact removal techniques requires both synthetic and real EEG data. Synthetic datasets are constructed by combining artifact-free EEG segments with manually extracted eye-blink artifacts, creating ground truth data where the precise artifact contribution is known [11]. Performance metrics including power spectrum ratio (Γ) and mean absolute error (MAE) quantify the effectiveness of artifact removal while assessing potential distortion of neural signals [11]. For microsaccade-related artifacts, validation includes correlation with high-resolution eye tracking and assessment of residual artifacts in the gamma frequency range where saccadic spike potentials are most prominent [14] [13].

Diagram 2: Ocular Artifact Propagation Pathway in EEG Research

Blinks, saccades, and microsaccades present distinct yet interconnected challenges for EEG research, each with characteristic generation mechanisms, topographic distributions, and methodological implications. Blinks produce high-amplitude frontal potentials, saccades generate spike potentials that contaminate gamma band activity, and microsaccades create both myogenic artifacts and genuine cortical responses. Comprehensive characterization of these artifacts enables development of more effective detection and removal strategies, including advanced machine learning approaches and signal processing techniques. Future research should focus on standardized validation frameworks and the integration of multimodal recording approaches to further disentangle ocular artifacts from neural signals of interest. For drug development professionals and neuroscientists, rigorous attention to ocular artifacts remains essential for ensuring the validity and interpretability of EEG findings across basic and applied research contexts.

Electroencephalography (EEG) is a fundamental tool for non-invasively investigating brain function, with applications spanning from basic cognitive neuroscience to clinical drug development. However, the low amplitude of neural signals (typically in the microvolt range) makes EEG highly susceptible to contamination from various sources of noise, collectively known as artifacts [19]. Among these, ocular artifacts (OAs)—generated by eye blinks and movements—represent one of the most pervasive and methodologically challenging problems in EEG data analysis. These artifacts introduce significant confounding signals that can obscure or mimic genuine neural activity, potentially compromising research validity and leading to erroneous conclusions [19] [9]. This technical guide examines the core issue of spectral and spatial contamination caused by ocular artifacts, with a specific focus on their overlap with the delta, theta, and alpha frequency bands. Furthermore, it details advanced methodological frameworks for their identification and removal, providing researchers with practical tools to enhance data integrity in neuroscientific and clinical research.

Spectral and Spatial Characteristics of Ocular Artifacts

Spectral Overlap with Neural Oscillations

The principal challenge of ocular artifacts lies in their extensive spectral overlap with key brain rhythms of interest. Unlike narrowband noise sources (e.g., powerline interference), OAs generate broadband signals that directly contaminate the canonical EEG frequency bands.

• Dominant Frequency Bands: Ocular artifacts exhibit peak spectral power in the delta (0.5–4 Hz) and theta (4–8 Hz) bands [19]. This is particularly problematic as these bands are critical for studying cognitive processes such as decision-making, memory, and attention, as well as clinical conditions like encephalopathy and sleep [20].
• Extended Spectral Influence: The spectral profile of a blink is not strictly limited to low frequencies. The box-shaped deflection caused by saccadic eye movements contains frequency components that can extend up to 20 Hz [21], creating substantial overlap with the alpha (8–13 Hz) band—the hallmark rhythm of the awake, relaxed brain and a key metric in many neuropharmacological studies [20].

Table 1: Spectral Characteristics of Ocular Artifacts and Overlapping Neural Functions

Spatial Topography and Amplitude

The spatial distribution of ocular artifacts is determined by the corneo-retinal potential dipole. When the eye moves or the eyelid closes during a blink, this dipole shifts, creating a large electrical field measurable across the scalp [19] [21].

• Maximum Impact Zone: The artifact is most prominent over frontal and prefrontal electrodes (e.g., Fp1, Fp2, F7, F8), where amplitudes can reach 100–200 µV—an order of magnitude larger than underlying cortical EEG [19] [21].
• Far-Field Effects: Although the voltage distribution follows a distance-dependent gradient, the artifact's influence is not confined to frontal regions. The propagated signal can significantly contaminate central and even parietal sites, complicating the analysis of neural generators outside the frontal lobe [21].

Diagram 1: Spectral and Spatial Contamination Pathways of Ocular Artifacts. This figure illustrates how artifacts from the corneo-retinal dipole propagate across key EEG frequency bands and scalp regions.

Methodologies for Ocular Artifact Detection and Removal

A range of techniques exists to mitigate ocular contamination, from classical regression-based approaches to modern data-driven and deep learning methods. The choice of method depends on factors such as the number of EEG channels, availability of EOG recordings, and computational resources.

Classical and Modern Signal Processing Approaches

• Independent Component Analysis (ICA): ICA is a widely used blind source separation technique that decomposes multi-channel EEG data into statistically independent components (ICs). Ocular artifacts, due to their distinct spatial and temporal properties, often segregate into specific ICs, which can be manually or automatically identified and removed from the data [22] [21]. ICA is highly effective but can be computationally intensive and may require expert supervision for component selection.
• Regression-Based Methods: These techniques use simultaneously recorded electrooculogram (EOG) signals to model the artifact's contribution to each EEG channel. The modeled artifact is then subtracted from the contaminated EEG. While effective, they often require dedicated EOG channels and may risk over-correction, removing parts of the genuine neural signal [21].
• Advanced Decomposition and Filtering: Newer hybrid methods demonstrate robust performance, particularly for challenging scenarios like single-channel EEG. One prominent example is the Fixed Frequency Empirical Wavelet Transform (FF-EWT) combined with a Generalized Moreau Envelope Total Variation (GMETV) filter [23]. This approach automatically decomposes the signal, identifies artifact-laden components using metrics like kurtosis and dispersion entropy, and applies a specialized filter to remove the artifact while preserving underlying brain activity [23].
• Spectral Signal Space Projection (S3P): This frequency-domain framework creates frequency-specific spatial projectors to suppress artifacts that have distinct topographies in narrowband oscillations. This is particularly advantageous for removing noise whose spatial pattern changes across frequencies, offering a targeted approach for denoising specific bands like delta, theta, or alpha [24].

Deep Learning Architectures

Recent advances in artificial intelligence have led to the development of calibration-free, end-to-end models for artifact removal.

• EEGOAR-Net: This deep learning model is based on a U-Net architecture and is trained to map contaminated EEG signals to their clean counterparts. A key innovation is its montage-independent design, achieved through a training methodology that masks different channels, making it flexible for use with various EEG setups. It operates without the need for subject-specific calibration or EOG reference channels, making it highly practical for real-time applications like brain-computer interfaces [25].

Table 2: Comparison of Ocular Artifact Removal Methodologies

Diagram 2: Workflow of Ocular Artifact Removal Methodologies. This diagram outlines the pathways from raw, contaminated EEG to a cleaned signal using different processing techniques.

Table 3: Key Research Reagents and Solutions for EEG Artifact Research

Failure to adequately address ocular artifacts can have profound consequences for data interpretation. Artifacts can reduce the signal-to-noise ratio, decreasing statistical power for detecting genuine neural effects [9]. More critically, they can introduce systematic confounds, potentially leading to false positives. For instance, a condition that elicits more frequent blinks (e.g., due to fatigue or cognitive load) could be misinterpreted as showing enhanced delta or theta power [19]. Recent research evaluating multivariate pattern analysis (decoding) has shown that while artifact correction does not always improve decoding performance, it remains essential to prevent artifact-related confounds from artificially inflating accuracy metrics and leading to incorrect conclusions [9].

Ocular artifacts present a formidable challenge in EEG research due to their significant spectral overlap with diagnostically and cognitively relevant frequency bands and their widespread spatial propagation across the scalp. A thorough understanding of their properties is a prerequisite for selecting an appropriate mitigation strategy. While established techniques like ICA and regression continue to be valuable, emerging methods from signal processing (e.g., FF-EWT) and deep learning (e.g., EEGOAR-Net) offer powerful, automated, and flexible alternatives. As EEG technology evolves toward greater portability and use in naturalistic settings [27], the development and adoption of robust, scalable artifact handling protocols will be paramount. By rigorously addressing the problem of ocular contamination, researchers in neuroscience and drug development can enhance the validity, reliability, and interpretability of their EEG data, solidifying the role of electrophysiology as a cornerstone tool for understanding brain function and its modification by pharmacological agents.

Electroencephalography (EEG) research is fundamentally constrained by the pervasive challenge of physiological artifacts, with ocular artifacts representing a predominant source of contamination. These artifacts introduce significant confounding variability, potentially biasing experimental outcomes in cognitive neuroscience, clinical diagnosis, and pharmaceutical development. This technical guide delineates the biophysical mechanisms through which ocular artifacts disproportionately affect frontal EEG channels, quantifies their impact on signal integrity, and evaluates contemporary methodological frameworks for their mitigation. Emphasis is placed on the implications for analysis reliability and the critical importance of targeted artifact management in upholding the validity of neuroscientific and clinical research findings.

Electroencephalography (EEG) provides unparalleled millisecond-scale temporal resolution for investigating brain dynamics, but its utility is contingent upon signal quality. The recorded microvolt-scale signals are exceptionally susceptible to contamination from non-neural sources, collectively termed artifacts [28] [19]. Among these, ocular artifacts (OAs)—generated by eye blinks and movements—are particularly problematic due to their high amplitude and spectral overlap with neural signals of interest [28] [29]. The inherent properties of OAs, including their generation via a robust bioelectric dipole and volume conduction through the head, result in a characteristic topographical distribution. This distribution is most pronounced over the frontal and frontopolar regions (e.g., Fp1, Fp2, F7, F8), which are also critical for assessing cognitive functions such as executive control and decision-making [30] [21]. Consequently, the accurate interpretation of frontal EEG activity is intimately linked to the effective management of OAs. Failure to address this contamination can lead to the misattribution of artifact-derived signals to neural processes, thereby compromising the integrity of research conclusions, from basic cognitive studies to clinical trials assessing neurotherapeutics.

The Core Biophysical Mechanisms

The profound impact of ocular artifacts on frontal channels is explained by two fundamental principles: the genesis of a high-amplitude electrical field and its projection via volume conduction.

The Corneo-Retinal Dipole

The primary source of ocular artifacts is the corneo-retinal potential, a steady electrical potential difference across the human eye. The cornea is electrically positive relative to the negatively charged retina, creating a stable electric dipole [19] [21]. During ocular events such as blinks or saccades, this dipole undergoes significant displacement and orientation changes.

• Eye Blinks: The closure of the eyelid causes a rapid movement of the corneal positivity towards the frontal scalp, inducing a large, positive-going slow potential deflection in the EEG recording [21].
• Eye Movements: Lateral saccades rotate the entire dipole, creating a field where one hemisphere (toward which the eyes move) becomes more positive while the other becomes more negative, resulting in a characteristic box-shaped waveform [21].

This dipole is robust, generating signals in the millivolt range (100–200 µV), which is orders of magnitude larger than the microvolt-scale cortical EEG signals it obscures [19] [30].

Volume Conduction and Projection to the Scalp

The electrical field generated by the corneo-retinal dipole propagates instantaneously through the head's conductive tissues (e.g., brain, cerebrospinal fluid, skull, skin) via volume conduction [30]. This process can be modeled as the passive spread of an electrical current from a point source. The strength of the recorded artifact at any given electrode is inversely proportional to the square of the distance from the source. Given the proximity of frontal electrodes to the ocular dipole, they experience the strongest signal. While the amplitude declines with greater distance, the artifact's influence is still measurable over posterior regions, albeit attenuated [21]. The following diagram illustrates this core signaling pathway.

Quantitative Impact and Signal Characteristics

The contamination of frontal channels by ocular artifacts is not merely topographic but has distinct, quantifiable signatures in both time and frequency domains, which are critical for detection and analysis.

Table 1: Characteristics of Ocular Artifacts in Frontal Channels

The quantitative disparity is stark. As noted in a 2025 study on dry EEG, the standard deviation of the signal—a measure of variability—can be dramatically reduced through effective artifact cleaning, underscoring the disproportionate influence of these contaminants on data metrics [31].

Methodologies for Investigation and Removal

A variety of experimental and computational methodologies have been developed to study and mitigate ocular artifacts. The choice of method often depends on the experimental setup, such as the number of available EEG channels.

Experimental Protocols for Ocular Artifact Analysis

Research into ocular artifacts often employs structured paradigms to elicit them in a controlled manner.

• Motor Execution Paradigm: A common protocol involves instructing participants to perform specific movements (e.g., hand, feet, or tongue movements) while EEG is recorded. This allows researchers to investigate the interaction between movement-related artifacts and ocular artifacts, particularly in mobile or dry EEG systems [31]. The protocol typically includes fixation crosses and cue arrows to standardize timing, with EEG recorded from a high-density cap (e.g., 64 channels) to capture full topographic spread.
• Controlled Elicitation: Simpler paradigms directly instruct participants to perform periodic eye blinks or saccades according to a visual cue. This generates a clean dataset of artifact events that can be used to train machine learning classifiers or validate removal algorithms [29].

Removal Techniques and Workflows

The workflow for handling ocular artifacts has evolved from simple rejection to sophisticated decomposition and machine learning approaches.

• Blind Source Separation (BSS) Techniques: Methods like Independent Component Analysis (ICA) are considered gold standards for multi-channel data. ICA decomposes the multi-channel EEG signal into statistically independent components (ICs). Ocular artifacts are typically isolated into one or a few ICs based on their characteristic topography (frontal focus), time course, and spectral properties. These artifactual ICs can then be subtracted from the data before signal reconstruction [31] [32] [30]. Studies show that targeted artifact reduction within components, rather than wholesale component subtraction, better protects against the artificial inflation of effect sizes (e.g., in ERPs) and source localization biases [32].
• Advanced Single-Channel Methods: For portable or single-channel systems where ICA is not feasible, complex pipelines have been developed. One state-of-the-art method integrates a Support Vector Machine (SVM) to first identify artifact-contaminated segments. These segments are then decomposed using Genetic Algorithm-optimized Variational Mode Decomposition (GA-VMD), followed by further source separation using the Second-Order Blind Identification (SOBI) algorithm. Components identified as artifacts via an approximate entropy threshold are removed before signal reconstruction [29]. The general workflow for such a pipeline is illustrated below.

Table 2: The Scientist's Toolkit: Key Reagents and Resources for Ocular Artifact Research

The vulnerability of frontal EEG channels to ocular artifacts is an immutable consequence of basic biophysics, driven by the high-amplitude corneo-retinal dipole and volume conduction. This phenomenon poses a persistent and significant challenge, threatening the validity of findings across neuroscience and drug development. Quantifying the impact—through amplitude, spectral, and topographic analysis—is a critical first step. Fortunately, the methodological arsenal available to researchers is powerful and evolving, ranging from well-established BSS techniques for dense-array data to innovative, machine-learning-driven pipelines for single-channel applications. The ongoing refinement of these methods, particularly those that move beyond simple subtraction to targeted cleaning, is paramount. Ensuring that EEG-based research conclusions are driven by neural signals, rather than ocular artifacts, requires diligent application of these sophisticated tools and a fundamental understanding of the principles of amplitude and projection.

In electroencephalography (EEG) analysis, the conventional wisdom holds that artifacts are a source of noise that obscures neural signals and diminishes analytical power. However, within the specific context of multivariate pattern analysis (MVPA), or decoding, a more complex and counterintuitive narrative emerges: systematic artifacts can artificially inflate decoding accuracy, leading to invalid conclusions about brain function. This whitepaper examines the mechanisms by which this inflation occurs, presents quantitative evidence of its effects, and provides methodological guidance for ensuring the validity of EEG decoding research, with a particular focus on ocular artifacts.

The core of the problem lies in the nature of decoding algorithms themselves. Unlike univariate analyses that assess signals at individual electrodes or time points, decoders like Support Vector Machines (SVMs) and Linear Discriminant Analysis (LDA) are designed to find any consistent pattern in the multidimensional data that distinguishes experimental conditions [18] [9]. If an artifact—such as an eye blink or muscle movement—occurs in a time-locked or condition-specific manner, the decoder can learn this artifactual pattern rather than, or in addition to, the underlying neural signal. Consequently, what appears to be successful decoding of a cognitive state may in fact be the successful decoding of a non-neural confound [34].

Mechanisms: How Artifacts Become Confounds

The Anatomy of an Inflated Decoding Result

Systematic artifacts inflate decoding performance through a direct confounding mechanism. For an artifact to cause inflation, two conditions must be met:

• The artifact must be large-amplitude and structured, possessing a consistent spatial or temporal signature that a classifier can detect.
• The artifact's occurrence must be correlated with the experimental conditions or task labels used to train the decoder.

Ocular artifacts are particularly potent confounds due to their high amplitude and the fact that eye movements and blinks are often intrinsically linked to cognitive tasks. For instance, in a visual attention task where a stimulus appears in the left versus right hemifield, participants may systematically make saccades toward the stimulus location. The resulting horizontal eye movement artifacts will be perfectly correlated with the task condition labels (left vs. right). A decoder may then achieve high accuracy by simply learning the characteristic pattern of the eye movement from frontal and temporal electrodes, rather than decoding the neural correlates of attention from occipital or parietal cortices [34] [21].

Table 1: Characteristics of High-Risk Artifacts in EEG Decoding

Experimental Evidence from Multiverse Analyses

Recent large-scale, systematic studies provide compelling quantitative evidence for artifact-induced inflation. A 2025 multiverse analysis published in Communications Biology systematically varied preprocessing steps across seven common EEG paradigms and assessed their impact on decoding performance using EEGNet and time-resolved logistic regression [34].

A critical finding was that artifact correction steps, including ICA, consistently reduced decoding performance. The authors identified specific scenarios where this was most pronounced:

• In the N2pc experiment, the removal of ocular artifacts strongly negatively impacted decoding performance for target position. This is because eye movements toward the target hemifield provided a systematic, non-neural signal that the decoder exploited [34].
• In the Lateralized Readiness Potential (LRP) experiment, removing muscle artifacts reduced decoding performance for hand-motor responses, suggesting that muscle activity related to the button press was itself informative to the classifier [34].

This demonstrates that when artifacts are systematically linked to the task, they become a reliable source of information for the decoder. Removing them reveals the true, and often lower, performance of the decoder when relying solely on neural signals.

Quantitative Impact: Assessing the Magnitude of Inflation

To understand the real-world impact, it is essential to examine quantitative data on how artifact correction influences key decoding metrics. The following table synthesizes findings from studies that directly compared decoding performance with and without rigorous artifact correction.

Table 2: Impact of Artifact Correction on EEG Decoding Performance

The data clearly show that the inflation is not a minor effect but can be substantial enough to form the primary basis for a decoder's success. Relying on uncorrected data in these scenarios leads to a fundamentally incorrect interpretation of what the decoder has learned.

Methodological Protocols for Controlled Investigation

For researchers seeking to validate their own findings, here are detailed protocols for conducting a controlled assessment of artifact impact.

Protocol A: The Artifact Inclusion/Exclusion Test

This protocol is the most direct way to test for artifact inflation [18] [34].

• Data Processing: Preprocess your EEG data through two parallel pipelines.
  • Pipeline 1 (Artifact-Retained): Apply only minimal preprocessing (e.g., filtering, re-referencing). Do not perform ICA, artifact rejection, or other artifact removal procedures.
  • Pipeline 2 (Artifact-Corrected): Apply a comprehensive artifact removal strategy. This should include ICA for ocular and muscle artifacts and potentially automated artifact rejection (e.g., Autoreject) for other large, non-stereotyped noises [34].
• Decoding Analysis: Train and test your chosen decoder (e.g., SVM, LDA) identically on the datasets from both pipelines, using the same cross-validation splits.
• Comparison: Statistically compare the decoding accuracy between the two pipelines.
  • Interpretation: If accuracy is significantly higher in the artifact-retained pipeline, it is strong evidence that artifactual signals are contributing to decoding performance. The corrected pipeline's accuracy is a more valid estimate of neural decoding.

Protocol B: Source-Specific Artifact Analysis

This protocol helps identify which type of artifact is responsible for inflation.

• Targeted Correction: Instead of one comprehensive correction pipeline, run multiple pipelines where you selectively remove specific artifacts.
  • Pipeline A: Remove only ocular components via ICA.
  • Pipeline B: Remove only muscular components via ICA.
  • Pipeline C: Remove both.
  • Pipeline D: No artifact removal (baseline).
• Differential Impact: Compare the decoding performance across all pipelines. A significant drop in performance after removing ocular components (Pipeline A) points to ocular artifacts as the primary confound. Similarly, a drop after Pipeline B implicates muscle artifacts.

Figure 1: Experimental workflow for Protocol A, the Artifact Inclusion/Exclusion Test. Comparing decoder performance between artifact-retained and artifact-corrected pipelines reveals potential inflation.

The Scientist's Toolkit: Key Reagents and Computational Solutions

To implement the protocols outlined above, researchers can leverage a suite of established software tools and methods.

Table 3: Essential Research Reagents for Artifact Management in Decoding

The pursuit of high decoding accuracy must not come at the cost of scientific validity. As evidenced, systematic artifacts, particularly ocular artifacts, pose a direct threat to the interpretability of EEG decoding studies by providing a non-neural pathway to high classification performance. To ensure robustness, researchers should adopt the following best practices:

• Always Report Correction Methods: Clearly detail the artifact correction procedures (or lack thereof) in any publication. This is a minimum standard for reproducibility and critical evaluation [39].
• Perform Control Analyses: Implement the protocols described in Section 4 as a standard control. Demonstrating that your results hold after rigorous artifact correction strengthens their validity.
• Prioritize Interpretability over Raw Performance: A slightly lower decoding accuracy based on genuine neural signals is far more valuable than a higher accuracy driven by confounds. As stated in [34], "uncorrected artifacts may increase decoding performance, this comes at the expense of interpretability and model validity."
• Leverage Advanced Tools: Utilize the toolkit in Section 5. For most research-grade EEG decoding, a combination of ICA for correction and SVM for decoding, followed by a comparison with uncorrected data, represents a robust methodological approach.

By acknowledging and actively controlling for this phenomenon, the field can move beyond obscuration and ensure that EEG decoding provides genuine insights into brain function.

A Practical Guide to Ocular Artifact Correction: From Traditional ICA to Deep Learning

Electroencephalographic (EEG) signals are perpetually vulnerable to contamination by ocular artifacts—electrical potentials generated by eye movements and blinks. These artifacts present a significant challenge in neurophysiological research and drug development due to three primary factors: their power spectrum (3–15 Hz) overlaps informatively with EEG theta and alpha bands; their frequency of occurrence is too high to permit simple epoch rejection without substantial data loss; and their amplitudes are dramatically larger than neural signals, potentially leading to misinterpretation of brain activity [1]. In the context of pharmacological studies, where EEG may serve as a biomarker for drug efficacy, undetected ocular artifacts can confound results by obscuring true neurophysiological signals or creating illusory treatment effects.

Numerous methods have been developed to correct these artifacts, ranging from simple rejection to advanced computational approaches. Among these, the regression-based procedure introduced by Gratton, Coles, and Donchin (1983) represents a foundational methodology that continues to influence contemporary EEG preprocessing pipelines [40]. This whitepaper provides an in-depth technical examination of the Gratton and Cole algorithm, detailing its underlying principles, practical implementation workflow, and limitations within modern research environments.

Principles of the Gratton and Cole Regression Method

The Gratton and Cole algorithm, formally termed the Eye Movement Correction Procedure (EMCP), operates on a core linearity assumption. It posits that the recorded signal on any EEG electrode is an additive combination of true brain activity and artifact contributions, which can be separated using a calculated propagation factor [40] [1].

Core Mathematical Model

The model is mathematically described as follows. For a given electrode ( e_i ) at time point ( n ):

[ \text{RawEEG}{ei}(n) = \text{EEG}{ei}(n) + \beta{ei} \cdot \text{artifacts}(n) ]

Here, ( \beta{ei} ) represents the artifact propagation factor specific to each electrode, quantifying how strongly the ocular artifact manifests at that recording site [1]. This factor varies across the scalp, typically exhibiting higher magnitudes at frontal sites closest to the eyes and decreasing toward parietal regions [40]. The procedure's key innovation was computing these propagation factors after removing stimulus-linked variability from both EEG and electrooculogram (EOG) traces, and deriving separate factors for blinks and eye movements based on data from the experimental session itself rather than a separate calibration [40].

Comparative Advantages in Research Settings

This approach offers distinct advantages for research contexts, particularly in clinical populations or paradigms where eye movements are part of the experimental task:

• Data Retention: Enables retention of all experimental trials, crucial for maintaining statistical power in studies with limited trials or populations characterized by frequent artifacts [40]
• Session-Specific Calibration: Computes propagation factors from experimental data rather than separate calibration sessions, enhancing ecological validity [40]
• Differential Correction: Applies separate correction factors for blinks and saccades, acknowledging their different electrical characteristics and scalp distributions [40]

Workflow and Implementation

Implementing the Gratton and Cole algorithm requires a systematic approach to ensure valid artifact correction. The following workflow synthesizes the original methodology with modern implementations found in contemporary toolkits like MNE-Python [41].

Data Preparation and Preprocessing

Proper data preparation is crucial for successful regression-based artifact correction:

• EEG Referencing: Apply a consistent reference scheme (e.g., average reference) before artifact correction [41]
• Filtering: Apply band-pass filtering (e.g., 0.3-40 Hz) to both EEG and EOG channels to remove slow drifts and high-frequency noise that can affect regression stability [41]
• Channel Selection: Identify appropriate EOG channels (vertical and horizontal) and EEG channels for correction

Table 1: Essential Research Reagents and Materials

Figure 1: Complete workflow of the Gratton and Cole regression method for ocular artifact correction, from data acquisition through validation.

Core Regression Procedure

The algorithm follows a structured procedure to estimate and remove ocular artifacts:

• Artifact Detection: Identify periods containing ocular artifacts (blinks and saccades) in the EOG channel [42]
• Propagation Factor Calculation: For each EEG electrode, compute the propagation factor (( \beta{ei} )) that describes the relationship between EOG and EEG traces during artifact periods, after first removing stimulus-locked variability from both signals [40]

• Artifact Subtraction: Remove the estimated artifact component from the continuous EEG data using the formula:

  [ \text{CorrectedEEG}{ei}(n) = \text{RawEEG}{ei}(n) - \beta{ei} \cdot \text{EOG}(n) ]

• Validation: Verify correction efficacy by comparing data before and after correction, typically through visual inspection of evoked potentials or quantitative metrics [41]

Methodological Variations

Modern implementations have introduced variations to enhance the original algorithm:

• Gratton's Evoked Response Subtraction: Subtract the evoked potential from each epoch before computing regression coefficients to isolate artifact-related variance [41]
• Croft & Barry's Blink Epoching: Create epochs time-locked to blink events and compute regression on the averaged blink response to improve coefficient estimation [41]

Performance and Limitations

While foundational, the Gratton and Cole algorithm has specific limitations that researchers must consider when selecting artifact correction methods.

Quantitative Performance Metrics

Table 2: Comparative Performance of Ocular Artifact Correction Methods

Specific Limitations and Considerations

The regression approach faces several critical limitations that affect its application in modern research:

• Crosstalk Contamination: EOG electrodes record not only ocular artifacts but also cerebral activity, particularly from frontal regions, potentially leading to over-correction and removal of genuine neural signals [41]
• Linearity Assumption: The method assumes a linear, time-invariant relationship between EOG and EEG channels, which may not fully capture the complex electromagnetic propagation of artifacts [1]
• Spatial Specificity: A single propagation factor per electrode may inadequately represent artifact dynamics across different experimental conditions or time points [40]
• Practical Constraints: The method is generally recommended for EEG rather than MEG data, as the relationship between artifact and signal differs in magnetic field recordings [41]

Comparative studies have revealed that while regression effectively reduces ocular artifacts, it may not match the performance of other methods in certain contexts. For instance, Wallstrom et al. (2004) found that adaptive filtering improved regression-based correction, and that PCA-based methods effectively reduced artifacts with minimal spectral distortion, while ICA sometimes distorted power in specific frequency bands [43].

Impact on Advanced Analytical Approaches

The choice of artifact correction method can significantly influence downstream analyses, particularly sophisticated analytical approaches increasingly used in pharmaceutical and cognitive neuroscience research.

Implications for Multivariate Pattern Analysis

Recent evidence suggests that artifact correction strategies interact critically with multivariate analytical approaches:

• Decoding Performance: A 2025 study examining SVM- and LDA-based decoding of EEG signals found that artifact correction combined with artifact rejection generally did not improve decoding performance across seven common ERP paradigms [9]
• Confound Management: Despite not enhancing performance, artifact correction remains essential to minimize the risk of artificially inflated decoding accuracy caused by artifact-related confounds rather than genuine neural signals [9]
• Trial Count Considerations: For multivariate analysis, preserving trial count through correction may be more advantageous than aggressive rejection, as sufficient trials are crucial for training robust decoders [9]

Figure 2: Relationship between artifact correction strategies and multivariate pattern analysis (MVPA) outcomes in EEG research.

The Gratton and Cole regression algorithm represents a historically significant and methodologically straightforward approach to ocular artifact correction in EEG research. Its core principles of linear artifact propagation and session-specific calibration continue to offer utility in specific research contexts, particularly those prioritizing trial retention and implementing minimal channel arrays.

Based on our technical examination, we recommend:

• Contextual Method Selection: Reserve regression-based methods for studies with limited channel counts or when maximal trial retention is paramount [1]
• Validation and Verification: Implement rigorous post-correction validation, particularly when using regression-corrected data in multivariate decoding paradigms [9]
• Hybrid Approaches: Consider combining regression with complementary techniques—using regression for initial ocular artifact reduction followed by other methods for remaining artifacts
• Transparent Reporting: Clearly document artifact correction procedures and parameters in research publications to enable proper evaluation and replication

Within the broader thesis of how ocular artifacts affect EEG data analysis, the Gratton and Cole algorithm highlights a fundamental tension in electrophysiological research: the imperative to remove confounding signals while preserving genuine neural data. As analytical techniques grow more sophisticated, the interaction between artifact correction strategies and research outcomes will continue to demand careful consideration, particularly in pharmaceutical development contexts where EEG may serve as a sensitive biomarker for treatment effects.

Independent Component Analysis (ICA) has established itself as a fundamental technique in the preprocessing pipeline for high-density electroencephalography (EEG). By separating mixed signals into statistically independent sources, ICA enables the effective identification and removal of pervasive ocular artifacts—such as blinks and eye movements—that would otherwise obscure neural signals. This technical guide explores the core principles of ICA, provides detailed experimental protocols for its application, and quantitatively demonstrates its efficacy in preserving data integrity. Framed within the broader challenge of how ocular artifacts affect EEG data analysis, this review underscores ICA's critical role in ensuring the validity and reliability of neuroscientific and clinical research.

Electroencephalography (EEG) provides unparalleled millisecond-scale temporal resolution for studying brain dynamics, but its signal is notoriously susceptible to contamination from non-neural sources. Among these, ocular artifacts are one of the most prevalent and challenging problems. Generated by the corneo-retinal dipole potential, eye blinks and movements produce large electrical potentials that can spread across the scalp, overwhelming the much smaller microvolt-level brain signals [44] [45]. These artifacts are especially problematic in cognitive experiments where participants are visually engaged or are not explicitly instructed to refrain from blinking, as such instructions can themselves alter brain activity [44].

The impact of these artifacts extends beyond simply adding noise; they can severely distort quantitative analyses and lead to spurious findings. For instance, artifact-contaminated signals can artificially inflate the apparent synchronization between channels or create false event-related potentials. Consequently, effective artifact remediation is not merely a technical preprocessing step but a foundational requirement for any rigorous EEG research. While various methods exist, from simple regression to advanced machine learning, Independent Component Analysis (ICA) has emerged as a particularly powerful and widely adopted solution for high-density EEG systems.

Theoretical Foundations of ICA

Core Mathematical Principle

Independent Component Analysis is a blind source separation (BSS) technique that aims to decompose a multivariate signal into additive, statistically independent sub-components. The fundamental assumption for EEG is that the signals recorded at the scalp (X) are linear, instantaneous mixtures of underlying brain and non-brain source activities (S), combined via an unknown mixing matrix (A). This relationship is formalized as:

X = A × S

The goal of ICA is to estimate an unmixing matrix (W) that inverts this process, recovering the original source signals as:

S = W × X

These recovered sources, S, are the Independent Components (ICs). ICA achieves this separation by optimizing the unmixing matrix to maximize the statistical independence of the components. This independence is typically measured by criteria such as kurtosis (the peakedness of the amplitude distribution) or through information-theoretic measures like mutual information minimization [44] [46]. The Infomax algorithm, a common implementation, iteratively adjusts W to maximize the entropy of the output, effectively separating super-Gaussian sources like neural signals from artifacts [46] [47].

Why ICA is Suited for High-Density EEG

The success of ICA is intrinsically linked to the use of high-density EEG systems (typically 64+ channels). The algorithm requires more sensors than significant underlying sources to achieve a stable and physiologically plausible decomposition. High-density arrays provide this spatial oversampling, allowing ICA to model the volume conduction of electrical fields through the scalp and skull more accurately. Each IC is characterized by two key features: (1) a time-course of its activity, and (2) a scalp topography (a fixed vector of weights specifying its projection to each sensor). The topography reflects how the source's electrical field is picked up across the electrode array, enabling the identification and removal of artifact-related components based on their characteristic spatial and temporal signatures [46] [48].

ICA in Practice: An Experimental Protocol

Implementing ICA effectively requires careful data preparation and a systematic workflow. The following protocol, applicable in toolboxes like EEGLAB and FieldTrip, ensures optimal decomposition and artifact removal [46] [48].

Preprocessing for Optimal ICA

The quality of the ICA decomposition is heavily dependent on the quality of the input data. Key preparatory steps include:

• Data Collection: Record continuous, high-density EEG data. A large amount of data is required; heuristics suggest the number of data frames (channels × time points) should be substantially larger than the square of the number of channels [49].
• Filtering: Apply a high-pass filter with a cutoff typically around 0.1-1 Hz to remove slow drifts that can violate ICA's stationarity assumption. However, avoid aggressive filtering that can distort artifact morphology [48].
• Bad Channel and Segment Rejection: Identify and interpolate profoundly noisy channels. Additionally, remove sections of data with infrequent, large-amplitude, atypical artifacts (e.g., SQUID jumps in MEG, muscle bursts) using tools like ft_databrowser or ft_rejectvisual, as these can dominate and degrade the decomposition [48].

Decomposition and Artifact Identification

Once the data is prepared, the decomposition and identification process begins.

Workflow for ICA-Based Artifact Removal

• Running ICA: Select an ICA algorithm (e.g., Infomax, Jader, AMICA) from the toolbox menu and apply it to the preprocessed data. The computation may take several minutes to hours depending on data size and algorithm choice. The output is a set of independent components equal to the number of input channels [46].
• Inspecting Components: Visually inspect each IC to identify those corresponding to ocular artifacts. This involves assessing:
  • Scalp Topography: Ocular artifacts typically show strong, focal projections in frontal regions [46] [48].
  • Time Course: The component's activity should show large, stereotypical deflections coinciding with eye blinks or movements visible in the raw data or EOG channels.
  • Power Spectrum: Ocular components are characterized by a smoothly decreasing (1/f-like) spectrum [46].
• Component Rejection and Reconstruction: After identifying artifact-related ICs, they are subtracted from the data. The remaining components are back-projected to the sensor space, resulting in a cleaned EEG dataset where the ocular artifacts have been effectively removed [46] [48].

Quantitative Assessment of ICA Efficacy

The performance of ICA in removing ocular and other artifacts has been quantitatively validated in multiple studies, demonstrating its superiority over traditional filtering and regression methods.

Table 1: Quantitative Outcomes of ICA Application in EEG Studies

Table 2: Impact of Artifact Correction on Multivariate Pattern Analysis (MVPA)

Advanced Applications and Integrations

The utility of ICA extends beyond simple artifact removal into more advanced analytical frameworks.

• Integration with Machine Learning: ICA serves as a critical preprocessing step for machine learning (ML) analyses of EEG. By providing cleaner neural signals, it allows ML models like Support Vector Machines (SVM) to learn more robust brain-state classifiers, as seen in studies discriminating math experts from novices [50]. Furthermore, a 2025 study confirmed that ICA correction prior to MVPA is essential to prevent artifact-related patterns from artificially inflating decoding accuracy [9] [18].
• ICA with Covariates: A novel extension integrates behavioral or clinical covariates directly into the ICA decomposition. For example, one study incorporated cognitive scores from the Woodcock-Johnson test alongside EEG connectivity data. This "augmented ICA" approach produced components with stronger and more robust brain-behavior correlations than conventional ICA followed by post-hoc correlation analysis [47].
• Nonlinear and Fractal Analysis: Cleaned EEG data from ICA enables the application of sophisticated nonlinear analyses, such as the Higuchi Fractal Dimension (HFD), which quantifies signal complexity. HFD has been successfully used to classify cognitive states and expertise, a task that would be compromised by the presence of large ocular artifacts [50].

Table 3: Key Software and Analytical Tools for ICA in EEG Research

Within the critical context of mitigating ocular artifact contamination, Independent Component Analysis has rightfully earned its status as a gold standard in high-density EEG analysis. Its capacity to isolate and remove artifacts based on their statistical and spatial properties, without discarding valuable data epochs, makes it an indispensable tool. The quantitative evidence confirms that ICA effectively cleanses data with minimal distortion to neural signals, thereby safeguarding the integrity of subsequent analyses—from basic ERP examination to advanced machine learning and nonlinear dynamics. As EEG research continues to evolve towards more naturalistic paradigms and data-driven approaches, the role of ICA as a foundational pillar for ensuring data quality and interpretability will only become more pronounced.

Leveraging Eye Tracking for Objective and Automated ICA Component Selection

Ocular artifacts present a significant challenge in electroencephalography (EEG) research, potentially obscuring neural signals and compromising data integrity. While Independent Component Analysis (ICA) has emerged as a powerful method for isolating and removing these artifacts, the traditional approach of manual component selection introduces subjectivity, inconsistency, and scalability limitations. This technical guide explores the innovative integration of eye tracking to objectify and automate the ICA component selection process. We present a framework that uses precise, synchronized eye-movement data to definitively identify blink- and saccade-related independent components, thereby enhancing the reliability, efficiency, and accuracy of ocular artifact correction in EEG analysis.

Electroencephalography (EEG) provides unparalleled temporal resolution for studying brain dynamics but remains highly susceptible to non-neural artifacts, with ocular movements representing one of the most pervasive contamination sources. Eyeblinks and saccades generate electrical potentials that can dwarf cortical signals, particularly over frontal regions, potentially obscuring genuine neural activity and leading to misinterpretations [51].

The challenge extends beyond mere signal-to-noise ratio degradation. In multivariate pattern analysis (MVPA) and decoding approaches, artifacts can create spurious confounds if they are systematically related to experimental conditions [18] [34]. For instance, in paradigms where visual stimuli or motor responses elicit differential eye movements, classifiers may inadvertently learn these artifactual patterns rather than neural correlates of cognitive processes. This compromises both the validity and interpretability of findings, underscoring the critical importance of robust artifact handling methodologies.

Current State of ICA and Its Limitations

ICA Fundamentals for EEG Artifact Correction

Independent Component Analysis (ICA) is a blind source separation technique that decomposes EEG signals into statistically independent components (ICs), each characterized by a fixed scalp topography and an activation time course [46]. The underlying assumption is that artifacts and neural signals originate from distinct physiological processes and can therefore be separated. Once identified, artifactual ICs can be removed, and the remaining components can be back-projected to reconstruct cleaned EEG signals [51].

The standard EEGLAB workflow involves:

• Data Preparation: Filtering and removing bad channels/segments
• ICA Decomposition: Applying algorithms (e.g., Infomax, FastICA) to the data
• Component Inspection: Visual examination of IC properties (topography, time course, spectrum)
• Artifact Removal: Selecting and rejecting artifactual components
• Data Reconstruction: Creating artifact-corrected EEG [46]

The Subjectivity of Manual Selection

The critical limitation of this workflow lies in step 3—component inspection and selection. This process relies heavily on human expertise and subjective judgment. Trained analysts must visually sift through components, looking for characteristic signatures of ocular artifacts:

• Frontal, bipolar scalp distributions typical of eye blinks
• High-amplitude, transient deflections in the activation time course
• Smooth, low-frequency power spectra [46]

This manual approach is not only labor-intensive but also prone to error, particularly when non-artifactual frontally-maximal ICA components (e.g., those reflecting cognitive processes in prefrontal cortex) exhibit topographic distributions similar to blinks [51]. Inter-rater reliability can be variable, and the process becomes impractical for large-scale datasets.

Existing Automated Approaches and Their Shortcomings

Several automated methods have been developed to address these limitations:

Table 1: Current Automated ICA Component Selection Methods

While these automated approaches perform at or above the level of trained human observers [51], they share a fundamental vulnerability: they rely on inferred relationships rather than direct measurement of ocular activity. This inherent limitation creates an opportunity for a paradigm shift through direct integration of eye tracking.

Eye Tracking as a Ground Truth Signal

The Rationale for Integration

Eye tracking provides an objective, continuous measure of ocular behavior that can serve as ground truth for identifying artifact-related ICs. The core premise is straightforward: the IC(s) representing ocular artifacts should demonstrate activation time courses that are consistently and strongly correlated with actual eye movements and blinks as recorded by the eye tracker [52] [53]. This direct temporal correspondence offers a more principled basis for component selection than spatial topography or stereotypical statistical features alone.

This approach is particularly valuable for distinguishing genuine ocular artifacts from frontally-maximal neural signals, a known challenge for spatial-based automated methods [51]. By leveraging the precise timing information from eye tracking, researchers can resolve this ambiguity with high confidence.

Multimodal Datasets Enabling Development

The development of such integrated methodologies has been accelerated by the recent release of multimodal datasets that synchronously capture EEG, eye tracking, and sometimes even high-speed video:

Table 2: Multimodal Datasets for Method Development

These resources provide the necessary foundation for developing and validating eye tracking-informed algorithms by enabling direct correlation between measured gaze behavior and EEG components.

Experimental Protocol for Eye Tracking-Guided ICA

Hardware Setup and Synchronization

A. Equipment Requirements

• Research-grade EEG system with sufficient frontal coverage
• Synchronizable eye tracker (e.g., SR Research EyeLink, Tobii Pro)
• High-speed camera (optional, for validation) [52]
• Stimulus presentation computer

B. Critical Synchronization Procedure Precise temporal alignment between EEG and eye-tracking data streams is paramount. This is typically achieved via:

• TTL Triggers: The stimulus presentation computer sends simultaneous TTL pulses to both EEG and eye-tracking systems at trial onsets and key events [54].
• Network Synchronization: Alternatively, messages can be sent over ethernet to the eye tracker host PC, using its parallel port for triggering [54].
• Validation: Record a simple blink protocol where participants blink on cue; use these clear events to verify sub-sample alignment between systems.

Data Acquisition Parameters

EEG Recording

• Sampling Rate: ≥ 500 Hz to capture blink morphology
• Electrode Placement: Standard 10-10 system with emphasis on frontal sites (FP1, FP2, F7, F8, etc.)
• Reference: Appropriate reference scheme (e.g., linked mastoids, average reference)
• Impedance: Keep impedances below 5 kΩ for optimal signal quality

Eye Tracking Recording

• Sampling Rate: ≥ 250 Hz (preferably 500-1000 Hz)
• Parameters: Record gaze position (X, Y), pupil diameter, and blink detection flags
• Calibration: Perform rigorous calibration and validation before each recording session

Core Analysis Workflow

The following diagram illustrates the integrated preprocessing pipeline:

Quantitative Component Identification

The identification of blink-related ICs proceeds through these computational steps:

A. Eye Tracking Event Detection

• Blinks: Detect from pupil diameter traces using threshold-based algorithms or vendor software
• Saccades: Identify using velocity-based algorithms (e.g., Engbert & Kliegl, 2003)
• Create Event Markers: Precise timestamps for each detected ocular event

B. Temporal Correlation Analysis For each independent component (IC), calculate:

• Cross-Correlation: Between IC activation time course and eye tracking blink/saccade indicators
• Trial-Based Averaging: Create blink-locked ERPs for each IC
• Statistical Thresholding: Establish significance thresholds for correlation values

C. Objective Selection Criteria A component is classified as artifactual if it meets these criteria:

• Significant cross-correlation (e.g., r > 0.5, p < 0.001) with blink events
• Blink-locked average shows characteristic waveform morphology
• Frontal scalp topography consistent with ocular origin

The Scientist's Toolkit

Table 3: Essential Research Reagents and Solutions

Validation and Performance Metrics

Establishing Methodological Rigor

Validation should assess both the accuracy of component identification and the impact on downstream analysis:

Component-Level Validation

• Precision/Recall: Compare against manual coding by multiple expert raters
• False Positive Rate: Ensure frontal neural components are not incorrectly removed
• Spatial Topography: Verify identified components exhibit characteristic blink distributions

Data-Level Validation

• Artifact Reduction: Quantify reduction in blink-related variance in frontal channels
• Signal Preservation: Ensure neural signals of interest are preserved (e.g., ERPs)
• Decoding Performance: For MVPA, confirm that removal doesn't eliminate predictive neural features [18]

Impact on Decoding Performance

Recent evidence suggests that while artifact correction is essential for valid interpretation, its impact on decoding performance is nuanced:

Table 4: Impact of Artifact Correction on EEG Decoding Performance

These findings highlight a crucial consideration: automated component selection must be precise. Overly aggressive removal of components may eliminate predictive neural information, while insufficient correction allows classifiers to exploit artifactual patterns, compromising interpretability. Eye tracking-guided approaches offer the precision needed to navigate this tradeoff effectively.

The integration of eye tracking with ICA represents a significant advancement in objective ocular artifact correction for EEG research. By providing a ground truth signal for component identification, this approach addresses fundamental limitations of both manual selection and purely data-driven automated methods. The resulting framework enhances reproducibility, scalability, and accuracy in EEG preprocessing.

Future developments in this domain will likely focus on several key areas:

• Real-Time Applications: Streamlined processing for BCI and neurofeedback implementations
• Deep Learning Integration: End-to-end models that jointly process EEG and eye-tracking signals
• Cross-Paradigm Generalization: Robust algorithms that perform consistently across diverse experimental contexts
• Consumer-Grade Hardware: Adaptation for mobile EEG and eye-tracking systems [53]

As multimodal recording becomes increasingly accessible, eye tracking-guided ICA promises to become a standard methodology for ensuring the validity and interpretability of EEG research across basic neuroscience, clinical applications, and drug development.

Artifact Subspace Reconstruction (ASR) for Real-Time and Wearable Applications

Artifact Subspace Reconstruction (ASR) has emerged as a pivotal algorithm for handling artifacts in electroencephalographic (EEG) data, particularly for real-time applications and studies using wearable devices. This adaptive, component-based method effectively removes transient or large-amplitude artifacts contaminating EEG signals, making it suitable for both offline analysis and online real-time applications such as clinical monitoring and brain-computer interfaces (BCIs) [55]. The growing importance of ASR is directly linked to the expansion of wearable EEG technology into new domains, including healthcare, well-being, professional sports, and industrial settings [38]. These portable systems enable monitoring in real-world environments but introduce significant signal quality challenges due to uncontrolled settings, subject mobility, and the use of dry electrodes [38]. In these contexts, ocular artifacts remain one of the most pervasive and problematic noise sources, capable of severely compromising EEG analysis and interpretation. ASR provides a robust mathematical framework for handling these and other artifacts, making it an essential tool in the modern neurotechnologist's arsenal.

Core Principles of ASR and Recent Algorithmic Advances

Fundamental ASR Mechanism

ASR is an adaptive method for the online or offline correction of artifacts in multichannel EEG recordings. The core principle relies on learning a statistical model from clean calibration data and using this model to detect and reconstruct artifact-contaminated segments in new data. The algorithm operates by repeatedly computing a principal component analysis (PCA) on covariance matrices to detect artifacts based on their statistical properties in the component subspace [56]. Essentially, ASR assumes that non-brain signals induce a large amount of variance in the EEG data and can therefore be detected based on their deviant statistical properties compared to the calibration baseline [56].

The algorithm consists of two primary phases: calibration and processing. During the calibration phase, a robust covariance matrix is computed from clean reference data, typically at least one minute of artifact-free EEG recorded from the participant during rest under comparable recording conditions [56]. This covariance matrix is then decomposed via PCA to obtain eigenvectors and eigenvalues that define the "normal" subspace of brain activity. During the processing phase, ASR analyzes incoming data in short segments (default: 500 ms), computes their covariance matrices, and projects them into the component space defined during calibration. Components that exceed a statistically defined threshold are identified as artifacts and reconstructed using the clean eigenvectors from the calibration data [56].

Evolution of ASR Algorithms

Recent research has focused on addressing limitations of the original ASR implementation (ASRoriginal), particularly its performance with non-stationary noise during intense real-world motor tasks and its dependency on high-quality calibration data [57]. These efforts have yielded several enhanced ASR variants:

• ASRDBSCAN and ASRGEV: These approaches introduce new methods for defining high-quality calibration data using point-by-point amplitude evaluation to eliminate collateral rejection of clean data, which was identified as a major cause of ASRoriginal's limitations. ASRDBSCAN uses a non-parametric Density-Based Spatial Clustering approach, while ASRGEV employs a parametric Generalized Extreme Value distribution [57].

• Riemannian ASR (rASR): This modification replaces the standard Euclidean geometry used in original ASR with Riemannian geometry for covariance matrix processing. Since covariance matrices are symmetric positive definite (SPD) matrices that lie in a curved, high-dimensional data space, Riemannian geometry provides more precise computations [56].

Table 1: Comparison of ASR Algorithm Variants

Performance Evaluation and Quantitative Comparisons

ASR Parameter Optimization

The effectiveness of ASR is highly dependent on the proper selection of its key parameters, particularly the standard deviation cutoff threshold. Systematic evaluation on EEG recordings from simulated driving experiments has demonstrated that the optimal ASR parameter typically falls between 20 and 30, effectively balancing the removal of non-brain signals with the retention of brain activities [55]. This cutoff value determines how aggressively the algorithm identifies data segments as artifacts, with higher values being more conservative and lower values being more aggressive in artifact removal.

Quantitative Performance Metrics

Recent studies have provided comprehensive quantitative assessments of ASR performance across multiple dimensions:

Table 2: Performance Comparison of ASR Algorithms

Empirical results from 205-channel EEG recordings during a three-ball juggling task (n=13) demonstrated that ASRDBSCAN found 42% and ASRGEV found 24% of data usable for calibration on average, compared to only 9% by ASRoriginal [57]. Subsequent Independent Component Analysis (ICA) showed that data preprocessed with ASRDBSCAN and ASRGEV produced brain ICs that accounted for more variance of the original data (30% and 29% respectively) compared to ASRoriginal (26%) [57].

In direct comparisons between ASR and rASR using EEG data recorded on smartphones in both outdoors and indoors conditions (N=27), the Riemannian version performed favorably on three key measures: reduction of eye-blinks (sensitivity), improvement of visual-evoked potentials (VEPs) (specificity), and computation time (efficiency) [56].

ASR Implementation for Real-Time and Wearable Systems

Integration in Wearable EEG Frameworks

The development of wearable high-density dry electrode EEG systems has created new opportunities for mobile brain monitoring in real-world environments. These systems typically integrate a compact EEG form-factor with wireless data streaming for online analysis [58]. A real-time software framework applied to such systems often includes adaptive artifact rejection (frequently via ASR), cortical source localization, multivariate effective connectivity inference, data visualization, and cognitive state classification [58].

A key advantage of ASR in these contexts is its compatibility with online processing. Unlike traditional artifact rejection methods like Independent Component Analysis (ICA), which although possible to use online is computationally demanding and designed primarily for offline use [56], ASR has been specifically engineered for real-time application. It processes data in chunks of 500 ms, resulting in very short processing delay and relatively low computational complexity [56]. This makes it well-suited for wearable systems with limited processing capabilities.

Handling Motion Artifacts in Mobile Environments

Wearable EEG systems present particular challenges for artifact handling due to fewer channels, restricted computational capabilities, and lower signal-to-noise ratio compared to traditional laboratory systems [59]. Additionally, artifacts in wearable EEG exhibit specific features due to dry electrodes, reduced scalp coverage, and subject mobility [38]. Motion artifacts are particularly problematic in mobile recordings and can be misinterpret as physiological events of interest, such as epileptic seizures [59].

Research has demonstrated that ASR-based pipelines are widely applied for handling various artifact types in wearable systems, including ocular, movement, and instrumental artifacts [38]. The algorithm's ability to adaptively learn the statistical properties of clean EEG from short calibration periods makes it particularly valuable for wearable applications where signal characteristics may change rapidly due to movement and environmental factors.

Experimental Protocols and Methodologies

Standard ASR Implementation Protocol

Implementing ASR for research applications follows a standardized workflow that can be adapted for both offline and online processing:

Protocol for Validating ASR Performance

Researchers have developed specialized experimental protocols to quantitatively evaluate ASR performance, particularly for challenging real-world scenarios:

• Data Collection: Record EEG data during both controlled tasks and real-world activities. For example, use a three-ball juggling task to induce high-intensity motion artifacts [57] or collect data during both standing and walking conditions indoors and outdoors [56].

• Reference Method Comparison: Compare ASR performance against established artifact handling methods such as ICA. Apply both ICA and an independent component classifier to separate artifacts from brain signals to quantitatively assess ASR's effectiveness [55].

• Quantitative Metrics: Evaluate performance using multiple metrics including:

  • Amount of usable data retained after cleaning
  • Power of artifact components before and after cleaning
  • Quality of subsequent ICA decomposition
  • Signal-to-noise ratios for evoked potentials
  • Computation time requirements [55] [56]

• Parameter Optimization: Systematically test different parameter settings, particularly the standard deviation cutoff value, to determine optimal values for specific recording conditions and research objectives [55].

Table 3: Essential Research Reagents and Resources for ASR Implementation

Artifact Subspace Reconstruction represents a significant advancement in handling artifacts for real-time and wearable EEG applications. The continued evolution of ASR algorithms—from the original implementation to newer approaches like ASRDBSCAN, ASRGEV, and rASR—demonstrates the research community's focus on addressing the unique challenges presented by mobile brain imaging. As wearable EEG technology continues to expand into new biomedical and consumer applications, robust and efficient artifact handling methods like ASR will play an increasingly critical role in ensuring data quality and interpretability. The quantitative performance data and standardized protocols presented in this review provide researchers with the necessary foundation to effectively implement ASR in their own experimental paradigms, particularly those investigating ocular artifacts and their impact on EEG data analysis.

Ocular artifacts (OA), primarily caused by eye blinks and movements, represent a significant contaminant in electroencephalography (EEG) data, obscuring crucial neural information and compromising analysis in both clinical and research settings. Traditional artifact removal methods often rely on electrooculography (EOG) reference channels or require subject-specific calibration, making them impractical for real-world applications like brain-computer interfaces (BCIs). This whitepaper examines the transformative impact of two emerging deep learning architectures—EEGOAR-Net and Bidirectional Long Short-Term Memory (BiLSTM) networks—in enabling effective, calibration-free removal of ocular artifacts. We provide an in-depth technical analysis of their operational mechanisms, present structured quantitative performance data, and detail experimental protocols. Framed within the broader challenge of preserving EEG data integrity, this review highlights how these data-driven models enhance the feasibility of robust, real-time EEG analysis for drug development and neuroscientific research.

Electroencephalography (EEG) is a cornerstone non-invasive technique for recording brain electrical activity, boasting high temporal resolution and wide application in clinical diagnosis, cognitive neuroscience, and brain-computer interfaces (BCIs) [60] [1]. However, the low amplitude of neural signals makes EEG highly susceptible to contamination by various artifacts, among which ocular artifacts (OA) are the most common and disruptive [25]. These artifacts originate from the corneo-retinal dipole, eyelid movements, and extraocular muscles, generating high-amplitude potentials that can be ten times greater than the underlying neural signals [60] [1].

The principal challenge in OA removal lies in the spectral and temporal overlap between artifacts and neural signals. Ocular artifacts predominantly affect the 3–15 Hz frequency band, which critically overlaps with the theta (4–7 Hz) and alpha (8–13 Hz) brain rhythms, which are essential for cognitive and emotional state analysis [1]. Simply discarding contaminated EEG segments leads to an unacceptable loss of neural information, given the high frequency of blinks (12–18 times per minute) [1]. Consequently, advanced signal processing techniques are required to separate and remove the artifact component while preserving the integrity of the neural signal, a process crucial for accurate data interpretation in research and clinical diagnostics [60].

Limitations of Traditional Ocular Artifact Removal Methods

Traditional methodologies for OA removal have significant limitations that hinder their application in modern, real-time systems.

• Regression-Based Methods: These linear approaches use EOG channels as a reference to subtract artifact components from EEG signals [1]. Their performance is heavily dependent on the availability and quality of a separate EOG reference, and they often suffer from mutual contamination, where neural activity is inadvertently removed along with the artifact [60].
• Blind Source Separation (BSS): Techniques like Independent Component Analysis (ICA) decompose multi-channel EEG into independent components, allowing for manual or semi-automatic identification and removal of OA-related components [1] [61]. While effective in high-density EEG systems (>40 channels), BSS methods require manual intervention, lack generalizability, and perform poorly with low-channel counts [60] [62].
• General Challenges: A common limitation across many traditional and hybrid methods is their reliance on manual parameter tuning, subject-specific calibration, or threshold-based heuristics. This limits their scalability, generalizability across diverse datasets, and suitability for real-time BCI applications where calibration is impractical [60] [25].

Deep Learning for EEG Denoising: Core Principles

Deep Learning (DL) models have emerged as powerful tools for EEG denoising due to their ability to learn complex, non-linear mappings directly from data without relying on pre-defined reference signals or statistical assumptions [60]. In the context of OA removal, a DL model is trained to approximate a function ( \varvec{f}_{\varvec{\theta}} ) that maps a noisy EEG signal ( \varvec{y} ) to an estimate of the underlying clean signal ( \varvec{x} ), where ( \varvec{y} = \varvec{x} + \varvec{z} ) and ( \varvec{z} ) represents the ocular artifact [60].

The model learns its parameters ( \varvec{\theta} ) (weights and biases) by minimizing a loss function, most commonly the Mean Squared Error (MSE), between its output ( \varvec{f}{\varvec{\theta}}(\varvec{y}) ) and the ground-truth clean signal ( \varvec{x} ) [60]: [ \mathcal{L} = \frac{1}{\varvec{n}} \sum{\varvec{i}=1}^{\varvec{n}}{({\varvec{f}}{\varvec{\theta}}\left({\varvec{y}}{\varvec{i}}\right)-{\varvec{x}}_{\varvec{i}})}^{2} ] Optimization algorithms like Adam or RMSProp are used to iteratively reduce this loss during training, enabling the network to discern and subtract the complex patterns of ocular artifacts from the raw EEG input [60].

In-Depth Analysis of Emerging Deep Learning Architectures

EEGOAR-Net: A Montage-Independent U-Net Architecture

EEGOAR-Net is a novel DL architecture specifically designed for calibration-free OA reduction, built upon the U-Net framework which is renowned for its efficacy in image-to-image translation tasks [25].

• Core Architecture: The model employs an encoder-decoder structure with skip connections. The encoder path progressively reduces the spatial dimensions of the input EEG while extracting high-level features. The decoder path subsequently reconstructs the clean EEG signal. The skip connections bridge the encoder and decoder, preserving fine-grained temporal details that might be lost during downsampling, which is crucial for maintaining the integrity of the neural signal [25].
• Key Innovation - Montage-Independent Training: A groundbreaking feature of EEGOAR-Net is its novel training methodology. The model is trained by randomly masking signals from different channels, forcing it to learn to rely on the intrinsic spatial and temporal characteristics of the artifact rather than on a fixed electrode montage. This enables the trained model to generalize effectively across different EEG systems with varying numbers and placements of electrodes, without requiring retraining or calibration [25].
• Operational Workflow: After training, EEGOAR-Net operates in a purely data-driven manner. It takes segments of contaminated multi-channel EEG as input and directly outputs the corresponding clean EEG, requiring no EOG reference or blink detection algorithm [25].

The following diagram illustrates the workflow and core innovation of EEGOAR-Net's training process:

BiLSTM with Time-Frequency Analysis: The WSST-Net Model

Bidirectional Long Short-Term Memory (BiLSTM) networks are another powerful architecture for OA removal, excelling at capturing long-range temporal dependencies in sequential data like EEG signals [63].

• Core Architecture: A BiLSTM consists of two LSTMs processing the input sequence in forward and reverse directions. This allows the network to utilize contextual information from both past and future states for every point in the EEG time series, providing a richer understanding of the signal's dynamics and improving artifact detection [63].
• Key Innovation - Wavelet Synchrosqueezed Transform (WSST): A specific implementation, termed WSST-Net, enhances the BiLSTM approach by first extracting highly localized time-frequency (TF) coefficients using the WSST. The WSST provides a superior time-frequency representation compared to traditional methods like Short-Time Fourier Transform (STFT) or Continuous Wavelet Transform (CWT), offering sharper energy concentration. These refined TF features are then fed into the BiLSTM network, which learns the complex mapping between the artifact-contaminated TF representation and that of the clean EEG [63].
• Operational Workflow: The process involves transforming the raw EEG into the time-frequency domain using WSST. The BiLSTM model then processes these TF features to predict the clean output, which is finally reconstructed back into the time domain [63].

The following diagram illustrates the signal processing pathway of the WSST-Net model:

Other Noteworthy Architectures

The DL landscape for EEG denoising is diverse. Other prominent models include:

• Generative Adversarial Networks (GANs): Frameworks like EEGANet use a generator network to produce clean EEG from noisy input and a discriminator to distinguish between generated and real clean signals. This adversarial training encourages the generator to produce highly realistic, artifact-free outputs [64] [65].
• Hybrid CNN-LSTM Models: Architectures such as CLEnet integrate Convolutional Neural Networks (CNNs) to extract spatial/morphological features and LSTMs to capture temporal dependencies, offering a comprehensive approach to signal analysis [62].

Quantitative Performance Comparison

The performance of deep learning models for OA removal is quantitatively assessed using standardized metrics that evaluate both the fidelity of the cleaned signal to the ground truth and the improvement in signal quality.

• Correlation Coefficient (CC): Measures the linear relationship between the cleaned and ground-truth signals. A higher CC (closer to 1) indicates better preservation of neural information [65] [62].
• Signal-to-Noise Ratio (SNR) & Signal-to-Artifact Ratio (SAR): Quantify the improvement in signal quality after denoising. Higher values indicate more effective artifact suppression [65] [62].
• Root Mean Square Error (RMSE) / Relative RMSE (RRMSE): Measure the magnitude of difference between the cleaned and ground-truth signals. Lower values indicate superior denoising performance [62] [66].

The table below summarizes the reported performance of the featured models against benchmarks.

Table 1: Quantitative Performance of Deep Learning Models for Ocular Artifact Removal

Detailed Experimental Protocols and Methodologies

To ensure reproducibility and provide a clear framework for validation, this section outlines the standard experimental pipeline for training and evaluating deep learning models for OA removal.

Dataset Curation and Preprocessing

A critical first step is the creation of a benchmarking dataset containing pairs of artifact-contaminated and clean ("ground-truth") EEG signals.

• Semi-Synthetic Data Generation: A common approach involves linearly mixing clean EEG recordings from databases like EEGdenoiseNet or DEAP with pure EOG artifacts at varying Signal-to-Noise Ratio (SNR) levels [63] [62]. This allows for controlled, quantitative evaluation since the ground truth is known.
• Real EEG Data with Reference Methods: Alternatively, real contaminated EEG data can be used, where the "ground truth" clean signal is generated by applying a well-established reference method (e.g., a sophisticated ICA routine) to the raw data. This method was used to train EEGOAR-Net, using SGEYESUB as the reference [25].
• Preprocessing: Raw data is typically band-pass filtered (e.g., 1-40 Hz) to remove drifts and high-frequency noise. Data is then segmented into epochs and normalized [1].

Model Training and Evaluation Framework

• Training Phase: The model (e.g., EEGOAR-Net, BiLSTM) is trained in a supervised manner. Inputs are contaminated EEG segments, and the training target is the corresponding clean segment. The model's parameters are optimized by minimizing the MSE loss using algorithms like Adam [60] [25].
• Evaluation - Cross-Validation: A robust evaluation involves subject-independent cross-validation, where the model is trained on data from a subset of subjects and tested on completely unseen subjects. This rigorously assesses generalizability, a key challenge in the field [25].
• Evaluation - Benchmarking: The model's output is compared against the ground truth using the metrics in Table 1 (CC, SNR, RMSE). Performance is benchmarked against state-of-the-art methods, both traditional (e.g., ICA) and DL-based (e.g., 1D-ResCNN, IC-U-Net) [25] [62].

The Scientist's Toolkit: Essential Research Reagents

The following table details key computational resources and data tools essential for research in this domain.

Table 2: Key Research Resources for Deep Learning-Based EEG Denoising

The advent of deep learning models like EEGOAR-Net and BiLSTM-based WSST-Net marks a significant paradigm shift in the removal of ocular artifacts from EEG data. Their ability to operate in a calibration-free manner, without dependency on EOG references and while generalizing across electrode montages, directly addresses the critical limitations of traditional methods. This capability is invaluable for the practical deployment of EEG technology, particularly in real-time BCI applications and large-scale clinical or pharmacological studies where subject-specific setup is infeasible.

Future research will likely focus on several key areas [60]:

• Architectural Innovation: Development of more efficient hybrid models (e.g., combining transformers with CNNs) and exploration of attention mechanisms to further improve performance and computational efficiency.
• Learning Paradigms: Adoption of self-supervised and federated learning techniques to leverage vast amounts of unlabeled data while addressing data privacy concerns, which is particularly relevant in multi-center clinical trials.
• Real-World Validation: Increased emphasis on validating model performance on completely real, non-synthetic datasets under diverse and challenging recording conditions.

For researchers and professionals in drug development and neuroscience, these emerging DL tools offer a powerful means to ensure the integrity of EEG data. By providing cleaner neural signals, they enhance the reliability of biomarkers for assessing drug efficacy, understanding neurological disorders, and advancing cognitive research, ultimately paving the way for more precise and effective interventions.

Troubleshooting Artifact Correction: Strategy Selection and Performance Optimization

In electroencephalography (EEG) research, ocular artifacts—signals generated by eye movements and blinks—represent a pervasive challenge that can severely compromise data integrity. These artifacts introduce high-amplitude, low-frequency noise that obscures genuine neural activity, particularly from frontal brain regions [19]. The corneo-retinal potential dipole of the eye generates an electric field measurable on the scalp, producing artifacts that can reach 100–200 µV, often an order of magnitude larger than brain-generated EEG signals [19]. This contamination risk is particularly acute in research domains requiring precise temporal characterization of neural events, such as drug development studies investigating neurophysiological biomarkers or cognitive neuroscientists studying event-related potentials (ERPs).

Failure to adequately address ocular artifacts can lead to deceptive interpretation of underlying brain states [68]. Recent findings demonstrate that imperfect artifact removal can artificially inflate effect sizes in ERP analyses and bias source localization estimates, potentially leading to false positive findings in clinical research [32]. As EEG applications expand into real-world settings through wearable technology, researchers face increasingly complex decisions regarding artifact management strategies. This technical guide examines three critical decision factors—channel density, real-time processing needs, and EOG channel availability—to inform method selection within a comprehensive ocular artifact management framework.

Understanding Ocular Artifacts and Their Research Implications

Physiological Origins and Characteristics

Ocular artifacts primarily manifest through two mechanisms: eyeblinks and eye movements. The eye functions as an electric dipole with the cornea positively charged relative to the retina. When the eye moves or blinks, this dipole shifts orientation, creating a large electric field disturbance that spreads across the scalp [19]. Blinks typically generate symmetrical frontal potentials, while horizontal eye movements produce characteristic opposite-polarity patterns at lateral frontal sites.

The spectral signature of ocular artifacts dominantly affects lower EEG frequencies (0.5–12 Hz), creating significant overlap with cognitively relevant neural signals in the delta (0.5–4 Hz) and theta (4–8 Hz) bands [23] [19]. This spectral overlap presents a fundamental challenge for simple frequency-based filtering approaches, as removing artifact components inevitably risks eliminating genuine neural activity of interest.

Impact on Research Paradigms

The confounding effects of ocular artifacts extend across multiple EEG research domains:

• Event-Related Potential (ERP) Research: Ocular artifacts can mimic or obscure components including the P300, N400, and error-related negativity, potentially leading to misinterpretation of cognitive processes [9] [32].
• Brain-Computer Interfaces (BCIs): Artifact contamination reduces classification accuracy and impedes reliable system operation, particularly in portable applications [25].
• Clinical Drug Development: In pharmacological EEG studies, artifact-induced signal distortions can compromise the assessment of drug effects on brain dynamics and cognitive function.
• Connectivity Analysis: Ocular artifacts introduce spurious correlations between channels, invalidating functional connectivity metrics.

Table 1: Quantitative Impact of Ocular Artifacts on EEG Signals

Decision Factor 1: Channel Density and Montage Configuration

Channel count fundamentally constrains the available methodological approaches for ocular artifact removal, primarily due to its relationship with spatial information availability.

High-Density Systems (≥32 Channels)

High-density configurations provide sufficient spatial sampling for source separation techniques that leverage topographic information. Independent Component Analysis (ICA) represents the gold standard for these systems, effectively separating neural and artifactual sources based on statistical independence [38] [32]. The RELAX pipeline exemplifies advanced ICA implementation, incorporating targeted cleaning that applies correction specifically to artifact-dominated periods or frequencies, thus preserving neural data during clean segments [32].

Recent advances include the EEGOAR-Net deep learning model, which provides montage-independent processing through a novel training methodology that masks signals from different channels, enabling flexibility across various EEG configurations while maintaining performance [25].

Low-Density and Wearable Systems (≤16 Channels)

Wearable EEG systems typically employ 16 or fewer channels and often utilize dry electrodes, creating distinct artifact profiles characterized by increased motion artifacts and reduced spatial information [38] [69]. The limited channel count impedes effective ICA application, as successful source separation typically requires adequate spatial sampling [38].

Single-Channel Configurations

Single-channel EEG presents the most challenging scenario for ocular artifact removal, eliminating spatial information entirely. Consequently, methods must rely exclusively on temporal, spectral, or statistical properties of the signal. Recent approaches have integrated decomposition algorithms with specialized filtering techniques:

• FF-EWT + GMETV: Fixed Frequency Empirical Wavelet Transform identifies artifact components using kurtosis, dispersion entropy, and power spectral density metrics, followed by Generalized Moreau Envelope Total Variation filtering [23].
• VME-GMETV: Variational Mode Extraction isolates artifact segments, which are subsequently processed using a GMETV filter to recover neural content before recombination with unaffected signal portions [68].

Table 2: Method Selection Guide by Channel Density

Decision Factor 2: Real-Time Processing Requirements

The temporal constraints of a research paradigm significantly influence method selection, distinguishing between offline analysis that permits retrospective processing and real-time applications requiring immediate correction.

Offline Research Applications

In contexts without immediate time constraints, such as post-experiment analysis of ERP data or retrospective clinical studies, researchers can employ computationally intensive approaches that optimize signal quality without time pressure. ICA-based pipelines excel in these scenarios, particularly when implementing advanced techniques like the RELAX method, which targets artifact reduction specifically to contaminated periods or frequencies within components [32]. These approaches typically require several minutes of subject-specific EEG data for optimal decomposition and may involve manual component inspection.

Real-Time Applications

BCIs and neurofeedback systems necessitate artifact removal with minimal latency to maintain system responsiveness. Deep learning approaches have demonstrated particular promise for these applications, with architectures like EEGOAR-Net providing effective correction without subject-specific calibration [25]. The integration of convolutional neural networks with long short-term memory (LSTM) components, as implemented in CLEnet, captures both morphological and temporal features of EEG signals, enabling effective artifact separation suitable for real-time implementation [33].

Decision Factor 3: EOG Channel Availability and Reference Signals

The availability of dedicated electrooculography (EOG) channels significantly influences methodological possibilities, particularly for regression-based approaches and component validation.

With EOG Reference Channels

When dedicated EOG channels are available, regression methods in either the time or frequency domain can effectively model and subtract artifact contributions from EEG signals [19]. These approaches establish the relationship between EOG and EEG channels during artifact periods, then apply this transformation to remove artifacts. Similarly, adaptive filtering techniques require reference signals to model and subtract noise [68]. However, these methods face limitations including the need for separate recording channels, increased setup complexity, and the assumption of consistent artifact propagation [33].

Without EOG Reference Channels

Many modern research scenarios, particularly those using wearable systems or minimal montages, lack dedicated EOG channels. This constraint has driven development of blind source separation and deep learning approaches that operate without reference signals. ICA can separate neural and artifactual components without EOG reference, though component classification may benefit from additional validation [32]. Contemporary deep learning models like EEGOAR-Net and CLEnet are specifically designed for calibration-free operation, making them particularly suitable for reference-free environments [33] [25].

Table 3: Method Comparison by EOG Availability and Performance

Integrated Decision Framework and Experimental Protocols

Method Selection Workflow

Detailed Experimental Protocols

Protocol 1: CLEnet for Multi-Artifact Removal

CLEnet represents a dual-branch neural network integrating dual-scale CNN with LSTM and an improved EMA-1D (One-Dimensional Efficient Multi-Scale Attention) mechanism [33].

Workflow:

• Morphological Feature Extraction: Two convolutional kernels of different scales extract morphological features at different resolutions
• Temporal Feature Enhancement: EMA-1D modules capture cross-dimensional interactions while preserving temporal features
• Temporal Feature Extraction: Dimensionally reduced features processed through LSTM to capture temporal dependencies
• EEG Reconstruction: Flattened features reconstructed into artifact-free EEG via fully connected layers

Implementation Details:

• Training: Supervised approach using mean squared error (MSE) loss function
• Datasets: Validated on three datasets including semi-synthetic (EEGdenoiseNet) and real 32-channel EEG
• Performance: Achieved SNR of 11.498dB and CC of 0.925 for mixed artifact removal [33]

Protocol 2: Targeted ICA With RELAX

The RELAX method enhances traditional ICA by applying targeted cleaning to artifact components [32].

Workflow:

• Component Separation: Standard ICA decomposition to separate neural and artifactual sources
• Component Classification: Identify ocular and muscle artifact components using predefined criteria
• Targeted Cleaning: For eye movement components, apply cleaning only during artifact periods; for muscle components, remove only artifact-dominated frequencies
• Signal Reconstruction: Reconstruct data using cleaned components

Implementation Details:

• Effect: Reduces artificial inflation of ERP effect sizes and source localization biases
• Availability: Freely available as an EEGLAB plugin
• Validation: Tested across different EEG systems and cognitive tasks (Go/No-go, N400) [32]

Protocol 3: FF-EWT + GMETV for Single-Channel EEG

This automated approach combines Fixed Frequency Empirical Wavelet Transform with Generalized Moreau Envelope Total Variation filtering [23].

Workflow:

• Signal Decomposition: FF-EWT decomposes contaminated EEG into six intrinsic mode functions (IMFs)
• Artifact Identification: Identify EOG-contaminated IMFs using kurtosis, dispersion entropy, and power spectral density metrics
• Artifact Removal: Apply GMETV filter to remove artifacts from contaminated IMFs
• Signal Reconstruction: Reconstruct denoised EEG from processed IMFs

Implementation Details:

• Performance: Achieved RRMSE of 0.1557 and CC of 0.9695 on synthetic data
• Advantage: Does not require reference signal or manual threshold adjustment [23]

The Scientist's Toolkit: Research Reagent Solutions

Table 4: Essential Materials and Computational Tools for Ocular Artifact Research

Selecting appropriate ocular artifact removal strategies requires careful consideration of channel density, real-time requirements, and EOG availability within specific research contexts. High-density systems benefit from targeted ICA approaches like RELAX, while low-density and wearable configurations increasingly leverage deep learning models such as CLEnet and EEGOAR-Net that offer calibration-free operation. Single-channel environments necessitate sophisticated decomposition techniques like FF-EWT+GMETV that operate without spatial information. Across all configurations, the field is moving toward automated, targeted approaches that minimize neural signal loss while effectively addressing the unique challenges posed by ocular artifacts in EEG research. As methodological innovation continues, researchers must remain informed of emerging capabilities that enhance both signal quality and analytical validity in their specific experimental paradigms.

The field of electroencephalography (EEG) is undergoing a transformative shift from traditional laboratory settings to real-world applications through the advent of wearable technologies. Dry-electrode EEG systems with low-channel counts are at the forefront of this revolution, offering unprecedented opportunities for monitoring brain activity in natural environments beyond the constraints of clinical settings [70] [71]. These advancements are particularly valuable for clinical trials, neurorehabilitation, and daily brain monitoring, where minimizing patient and site burden is paramount [72]. However, this transition presents significant technical challenges, with ocular artifacts representing a particularly pervasive problem that can severely compromise data integrity and interpretation [1].

Ocular artifacts, generated by eye blinks and saccades, introduce high-amplitude signals that overwhelm neural data within the critical 3-15 Hz frequency range, directly overlapping with clinically relevant theta and alpha brain rhythms [1]. This interference is especially problematic in dry-electrode systems, which often have lower signal-to-noise ratios and fewer channels for spatial filtering compared to traditional high-density wet EEG systems [71] [72]. The corneo-retinal dipole - the positive charge of the cornea relative to the retina - creates potential field changes that propagate across the scalp, while eyelid movements and extraocular muscle contractions further contribute to these artifacts [1]. Addressing these contaminants is therefore essential for leveraging the full potential of wearable EEG technologies in both research and clinical applications.

Technical Challenges in Dry-Electrode and Low-Channel-Count Systems

Dry-electrode EEG systems represent a fundamental departure from traditional wet electrodes, eliminating the need for conductive gel through innovative structural designs and materials [71]. While this advancement enables quicker setup, improved portability, and suitability for long-term monitoring, it introduces specific technical constraints that complicate artifact management.

Signal Quality Limitations

The quantitative performance of dry-electrode EEG varies considerably across different applications and frequency bands. Recent benchmarking studies reveal that while these systems perform adequately for certain measures like quantitative resting-state EEG and P300 evoked activity, they face notable challenges with specific signal aspects [72]. Low-frequency activity (<6 Hz) and induced gamma activity (40-80 Hz) present particular difficulties for dry-electrode systems, potentially due to their intrinsic electrical properties and greater susceptibility to motion artifacts [72]. This frequency-specific performance variation must be carefully considered when designing studies and interpreting results.

The physical interface between dry electrodes and the scalp also presents ongoing challenges. Three primary dry electrode architectures have emerged:

• MEMS dry electrodes utilizing microneedle arrays to gently penetrate the stratum corneum
• Dry contact electrodes relying on structural design for direct scalp contact
• Dry non-contact electrodes operating through capacitive coupling without physical skin contact [71]

Each design represents a different trade-off between signal quality, user comfort, and practical implementation, with no single solution universally dominating across all applications.

Spatial Resolution Constraints

Low-channel-count systems (typically ≤32 channels) face inherent limitations in spatial resolution compared to traditional high-density EEG montages (often 64-256 channels). This reduced spatial sampling directly impacts the effectiveness of conventional artifact removal techniques that rely on spatial filtering and source separation principles. Independent Component Analysis (ICA), for instance, demonstrates optimal performance with higher channel counts (typically >40 channels), making it less effective for sparse arrays [1]. The limited spatial information also reduces the ability to distinguish cerebral activity from artifacts based on topographic patterns, necessitating alternative approaches specifically designed for low-channel scenarios.

Table 1: Performance Benchmarking of Dry-Electrode EEG Systems

Ocular Artifacts: Physiological Basis and Impact on EEG Analysis

Ocular artifacts represent one of the most significant confounding factors in EEG analysis, particularly problematic for wearable systems where participants engage in natural activities involving frequent eye movements. Understanding the physiological origins of these artifacts is essential for developing effective correction strategies.

Physiological Mechanisms

Three primary physiological sources contribute to ocular artifacts in EEG recordings:

• Corneo-retinal dipole: The electrical potential difference between the positively charged cornea and negatively charged retina creates a dipole field that changes orientation with eye movements, generating widespread potentials across the scalp [1].
• Eyelid movements: The act of blinking produces pronounced potential field changes as the eyelid slides across the corneal surface, typically lasting 100-400ms with amplitudes reaching 50-100μV in frontal regions [1].
• Extraocular muscle activity: Contractions of the muscles responsible for eye movement generate electrical potentials that contaminate EEG signals, particularly during saccades and smooth pursuit movements.

These mechanisms produce artifacts characterized by high-amplitude spikes (often 5-10 times greater than background EEG) with a frequency bandwidth (3-15 Hz) that directly overlaps with clinically relevant neural oscillations in the theta (4-7 Hz) and alpha (8-13 Hz) ranges [1]. This spectral overlap prevents simple frequency-based filtering from effectively separating artifacts from neural signals without substantial data loss.

Impact on Data Analysis and Clinical Interpretation

The presence of ocular artifacts has profound implications for EEG analysis across both research and clinical domains. For event-related potential (ERP) studies, blink artifacts can obscure or mimic components like the P300, potentially leading to erroneous conclusions about cognitive processing [1]. In clinical diagnostics, artifact-contaminated recordings may result in misdiagnosis of neurological conditions such as epilepsy if ocular spikes are misinterpreted as epileptiform activity [73]. For neurofeedback and brain-computer interface applications, artifacts can corrupt feature extraction algorithms, reducing classification accuracy and system performance [71].

The problem is particularly acute for dry-electrode systems, where the already compromised signal-to-noise ratio is further degraded by ocular artifacts. A recent study evaluating artifact correction methods found that uncorrected ocular artifacts can decrease statistical power for conventional univariate analyses and potentially lead to artificially inflated decoding accuracy in multivariate pattern analysis if not properly addressed [18] [9].

Artifact Correction Strategies for Low-Channel-Count Systems

The unique constraints of low-channel-count dry-electrode systems necessitate specialized artifact correction approaches. Traditional methods developed for high-density laboratory EEG often require modification or replacement with techniques specifically designed for sparse array configurations.

Regression-Based Methods

Regression-based approaches represent the most straightforward artifact correction strategy for low-channel-count systems. These methods operate on the principle that the recorded EEG signal represents a linear combination of true neural activity and artifact components [1]. The general model can be expressed as:

RawEEG(n) = EEG(n) + β × artifacts(n) [1]

Where β represents the channel-specific weighting coefficient quantifying how strongly artifacts affect each electrode position. Regression techniques require a reference artifact template, typically derived from either dedicated electrooculography (EOG) channels or frontal EEG electrodes that capture the strongest artifact expression [1].

Two primary regression implementations have been validated for low-channel-count scenarios:

• Time-domain regression: Estimates artifact influence in the temporal domain and subtracts scaled artifact templates from contaminated channels [1].
• Frequency-domain regression: Operates similarly but in the frequency domain, potentially offering advantages for specific artifact types with characteristic spectral signatures [1].

The major advantage of regression methods for wearable systems is their computational efficiency and minimal channel requirements, making them suitable for real-time implementation on resource-constrained hardware. However, they assume linearity and stationarity in artifact propagation, which may not always hold true in real-world recording environments.

Artifact Subspace Reconstruction (ASR)

Artifact Subspace Reconstruction (ASR) represents a more advanced approach specifically designed for noisy EEG recordings in mobile settings. This method employs statistical modeling to identify and reconstruct portions of data contaminated by artifacts [1]. ASR operates by:

• Calculating the covariance structure of clean reference data
• Identifying data segments where the covariance structure significantly deviates from this reference
• Reconstructing the contaminated segments using a mixture of surrounding clean data and spatial components

The adaptive nature of ASR makes it particularly suitable for real-world environments where artifact characteristics may change over time. The method can handle various artifact types beyond ocular contaminants, including muscle activity, motion artifacts, and transient electrode failures. For optimal performance with low-channel-count systems, ASR parameters typically require adjustment to account for the limited spatial information available for subspace decomposition.

Deep Learning Approaches

Recent advances in deep learning have introduced powerful new options for artifact removal, particularly through Generative Adversarial Networks (GANs) and hybrid architectures. These methods can learn complex nonlinear relationships between artifacts and neural signals without requiring explicit artifact templates [73].

The AnEEG model exemplifies this approach, incorporating LSTM (Long Short-Term Memory) layers within a GAN architecture to effectively capture temporal dependencies in EEG data while removing artifacts [73]. This model demonstrated superior performance compared to traditional techniques like wavelet decomposition, achieving lower normalized mean square error (NMSE) and higher correlation coefficients (CC) with ground-truth signals [73].

Other promising architectures include:

• GCTNet: Combining GAN-guided parallel CNN with transformer networks to capture both global and temporal dependencies [73].
• EEGENet: Specifically designed for ocular artifact removal under various eye movement conditions [73].
• GANs with temporal-spatial-frequency loss: Incorporating multiple domain information to guide the reconstruction of clean EEG signals [73].

While computationally intensive, these methods can be pre-trained and deployed for real-time operation, making them increasingly viable for wearable systems as edge computing capabilities advance.

Table 2: Artifact Correction Methods for Low-Channel-Count EEG Systems

Experimental Protocols for Method Validation

Rigorous validation of artifact correction methods requires standardized experimental protocols and performance metrics. Below we outline established methodologies for quantifying the effectiveness of artifact removal in dry-electrode, low-channel-count systems.

Data Acquisition and Experimental Design

Comprehensive benchmarking studies should incorporate both controlled artifact induction and naturalistic recording conditions to evaluate method performance across scenarios [72]. A recommended protocol includes:

• Resting-state recordings: 5 minutes eyes-open followed by 5 minutes eyes-closed to capture baseline blink activity and alpha oscillations [72].
• Systematic artifact induction:
  • Voluntary blinks (every 4-6 seconds for 2 minutes)
  • Horizontal and vertical saccades between visual targets
  • Smooth pursuit eye movements
• Task-based recordings incorporating event-related potentials (e.g., auditory oddball P300 paradigm) to assess artifact impact on cognitive measures [72].
• Naturalistic activities (e.g., reading, speaking, walking) to evaluate performance in real-world conditions.

This protocol should be implemented using both the dry-electrode system under investigation and a concurrent traditional wet EEG system as ground reference where feasible. Including simultaneous EOG recordings provides valuable artifact templates for regression-based methods and performance validation.

Performance Metrics and Statistical Analysis

Quantitative evaluation should employ multiple complementary metrics to provide a comprehensive assessment of artifact correction performance:

• Normalized Mean Square Error (NMSE): Quantifies deviation from ground-truth signals [73].
• Root Mean Square Error (RMSE): Measures absolute difference between corrected and clean signals [73].
• Correlation Coefficient (CC): Assesses preserved temporal structure [73].
• Signal-to-Noise Ratio (SNR): Evaluates overall signal quality improvement [73].
• Signal-to-Artifact Ratio (SAR): Specifically quantifies artifact suppression [73].

Statistical analysis should examine both within-subject and between-subject variability, with particular attention to potential method-by-condition interactions that might indicate context-dependent performance. Recent research indicates that while artifact correction doesn't necessarily improve decoding performance in all cases, it remains essential to minimize artifact-related confounds that might artificially inflate accuracy measures [18] [9].

The Scientist's Toolkit: Research Reagent Solutions

Implementing effective artifact correction strategies requires both hardware components and software tools specifically suited to the constraints of dry-electrode, low-channel-count systems. The following toolkit outlines essential resources for researchers working in this domain.

Table 3: Essential Research Toolkit for Dry-EEG Artifact Correction

Dry-electrode EEG systems with low-channel counts represent the future of practical, accessible brain monitoring beyond traditional laboratory environments. However, the pervasive challenge of ocular artifacts demands specialized correction strategies tailored to the unique constraints of these platforms. Through appropriate method selection—ranging from computationally efficient regression approaches for resource-constrained applications to sophisticated deep learning models where feasible—researchers can effectively mitigate artifact contamination while preserving neural signals of interest.

The successful implementation of these strategies requires careful consideration of both the specific research context and the technical capabilities of available systems. As wearable EEG technology continues to evolve, ongoing development of artifact handling methods will be crucial for realizing the full potential of these platforms across clinical, research, and consumer applications. By adopting the systematic approaches outlined in this technical guide, researchers can navigate the complexities of ocular artifact correction while advancing the field toward more robust and reliable brain monitoring solutions.

Electroencephalogram (EEG) data analysis provides a non-invasive window into brain function, playing crucial roles in cognitive neuroscience, neurological disorder diagnosis, and neuropharmaceutical development. However, the electrical signals originating from eye movements and blinks—known as ocular artifacts—pose a significant threat to data integrity. These low-frequency, high-amplitude signals manifest prominently in frontal electrodes and exhibit substantial spectral overlap with genuine neural activity, particularly in the delta (0.5-4 Hz) and theta (4-8 Hz) bands [23]. This overlap creates persistent challenges for researchers seeking to isolate true neural signatures from contamination.

The problem is particularly acute in single-channel EEG systems, which are increasingly deployed in portable healthcare monitoring, brain-computer interfaces, and long-term ambulatory studies due to their user-friendly, wearable designs [23] [74]. Unlike multi-channel setups that can leverage spatial information through techniques like Independent Component Analysis (ICA), single-channel recordings cannot exploit channel relationships for artifact separation [75]. This limitation has driven the development of sophisticated decomposition techniques that operate exclusively within the temporal and spectral domains of individual channels, with Fixed Frequency Empirical Wavelet Transform (FF-EWT) emerging as a particularly promising approach [23].

Technical Foundation: From Traditional Methods to Advanced Decomposition

The Limitations of Conventional Artifact Removal Approaches

Traditional artifact removal methods each present significant limitations for single-channel EEG applications:

• Independent Component Analysis (ICA): Requires multiple channels for effective separation, making it unsuitable for single-channel applications [23] [75].
• Adaptive Filtering: Often depends on reference signals from additional sensors (e.g., EOG channels), increasing system complexity and subject burden [75].
• Standard Wavelet Transform: Applies fixed basis functions that may not adapt optimally to non-stationary EEG characteristics [23].
• Regression Methods: Need calibration procedures and additional recording channels, limiting practical implementation [75].
• Empirical Mode Decomposition (EMD): Suffers from mode mixing issues where artifactual and neural content leak into multiple components [23].

The Evolution Toward Advanced Decomposition Techniques

Advanced decomposition methods represent a paradigm shift by adapting to the inherent characteristics of each specific EEG signal. Unlike predetermined basis functions, these techniques automatically identify relevant frequency boundaries within the signal, creating customized filters that more precisely separate artifactual from neural content [23] [76]. This adaptive approach proves particularly valuable for ocular artifacts, which, despite their stereotypical morphology, exhibit considerable inter-individual variability in amplitude, duration, and spectral composition.

Fixed Frequency Empirical Wavelet Transform: A Technical Deep Dive

Core Algorithm and Implementation

The Fixed Frequency Empirical Wavelet Transform (FF-EWT) represents an advanced signal processing technique that constructs adaptive wavelets specifically tailored to a signal's spectral components. The method operates through three systematic phases:

• Spectral Segmentation: The Fourier spectrum of the input EEG signal is partitioned into contiguous segments corresponding to specific oscillatory modes, with particular attention to the 0.5-12 Hz range where ocular artifacts predominantly occur [23].

• Empirical Wavelet Construction: For each segment bounded by frequencies [ωₗ, ωₗ₊₁], the method constructs bandpass filters using a framework based on Littlewood-Paley and Meyer wavelets. The scaling function (υ₁(θ)) and empirical wavelet function (γ₁(θ)) are defined as follows [23]:

The function Φ(φ,θₗ) = α[(|θ|-(1-φ)θₗ)/(2φθₗ)] ensures a smooth transition between adjacent frequency bands, where parameter φ guarantees the formation of a tight frame in T²(ℜ) [23].

• Signal Decomposition: The constructed filter bank is applied to the original EEG signal, producing a set of Intrinsic Mode Functions (IMFs) representing distinct oscillatory components present in the data.

Artifact Component Identification

Following decomposition, the algorithm automatically identifies artifact-laden components using a multi-metric approach:

• Kurtosis (KS): Detects components with peaked distributions characteristic of ocular artifacts
• Dispersion Entropy (DisEn): Quantifies signal complexity, with artifactual components typically exhibiting lower entropy values
• Power Spectral Density (PSD): Identifies components with dominant power in the characteristic ocular artifact frequency range (0.5-12 Hz) [23]

Components flagged by these criteria are processed through a Generalized Moreau Envelope Total Variation (GMETV) filter that selectively attenuates artifactual content while preserving neural information in adjacent frequency bands [23].

Workflow Visualization

The following diagram illustrates the complete FF-EWT artifact removal workflow:

Comparative Performance Analysis

Quantitative Benchmarking Against Established Methods

When evaluated on both synthetic and real EEG datasets, the FF-EWT+GMETV approach demonstrates superior performance across multiple metrics compared to traditional techniques:

The exceptional performance of FF-EWT stems from its frequency-focused approach that specifically targets the spectral regions where ocular artifacts dominate while preserving neural activity in adjacent bands [23]. The GMETV filter's ability to perform selective attenuation rather than complete component rejection further contributes to neural information preservation.

Impact on Downstream Analytical Applications

The efficacy of artifact removal techniques must ultimately be judged by their impact on subsequent EEG analysis. Recent research demonstrates that effective artifact correction significantly influences analytical outcomes:

• Event-Related Potential (ERP) Analysis: Targeted artifact reduction prevents both false positive inflation and genuine effect attenuation that can occur with component subtraction methods [32].
• Multivariate Pattern Analysis (MVPA): While artifact correction alone may not dramatically improve decoding accuracy for simple binary classification tasks, it remains essential for minimizing artifact-related confounds that might artificially inflate decoding accuracy [18] [9].
• Clinical Monitoring: In applications such as anesthesia depth monitoring, FF-EWT-derived artifact removal enables more reliable tracking of neural signatures despite the presence of occasional ocular artifacts [77].

Experimental Protocols for Method Validation

Synthetic Data Validation Protocol

Comprehensive validation of single-channel artifact removal techniques requires a multi-stage approach combining synthetic and real data:

• Clean EEG Extraction: Obtain artifact-free baseline EEG segments from periods of minimal ocular activity, verified via simultaneous EOG recording [23].

• Artifact Simulation: Generate synthetic ocular artifacts with characteristics matching real blink properties (duration: 100-400ms, amplitude: 50-200μV, frequency content: 0.5-12Hz) [23].

• Controlled Mixing: Combine clean EEG with simulated artifacts at varying signal-to-artifact ratios (SAR from -10 dB to +10 dB) to create ground-truth datasets [23].

• Algorithm Application: Process contaminated signals through the FF-EWT+GMETV pipeline and compare outputs to original clean EEG using quantitative metrics [23].

Real-World Performance Assessment

For validation with real EEG data, researchers should implement:

• Parallel Recording: Capture simultaneous EEG and EOG signals during tasks designed to elicit ocular artifacts (e.g., paced blinking, saccadic eye movements) [23].

• Expert Annotation: Have trained electrophysiologists identify artifact-contaminated epochs in the recorded data [78].

• Quantitative Comparison: Calculate performance metrics (SAR, CC, RMSE) between artifact-corrected segments and adjacent artifact-free baseline periods with similar spectral characteristics [23] [75].

Future Directions and Clinical Translation

The evolution of single-channel artifact removal continues with several promising developments:

• AI-Enhanced EEG Analysis: Integration of deep learning with decomposition techniques shows potential for achieving performance parity with multi-channel systems [74].
• Real-Time Hardware Implementation: Efficient Verilog HDL implementations of DWT-based methods enable integration into low-power, portable clinical monitoring devices [75].
• Multi-Modal Fusion: Combining FF-EWT with complementary techniques like spatio-spectral decomposition (SSD) may further improve artifact separation in challenging scenarios [79].
• Personalized Filter Banks: Developing subject-specific wavelet constructions that adapt to individual neurophysiological characteristics represents an emerging frontier [23].

Fixed Frequency Empirical Wavelet Transform represents a significant advancement in addressing the persistent challenge of ocular artifacts in single-channel EEG research. By combining adaptive frequency segmentation with targeted component filtering, FF-EWT achieves superior artifact removal while preserving neurologically relevant information. As portable EEG systems continue to expand into healthcare monitoring, brain-computer interfaces, and pharmaceutical development, robust single-channel artifact removal methodologies will play an increasingly critical role in ensuring data quality and analytical validity. The techniques detailed in this guide provide researchers with powerful tools to enhance EEG data integrity, ultimately supporting more reliable neuroscientific discovery and clinical application.

Electroencephalography (EEG) preprocessing is a critical determinant of data quality and analytical validity in neuroscientific research and clinical applications. Within the context of a broader thesis on how ocular artifacts affect EEG data analysis, this technical guide examines the synergistic roles of filtering and re-referencing as essential countermeasures. Ocular artifacts introduce substantial low-frequency, high-amplitude noise that confounds neural signal interpretation, necessitating sophisticated preprocessing approaches. Contemporary research demonstrates that preprocessing choices significantly impact downstream analytical outcomes, including decoding performance, effect size estimation, and source localization accuracy. This whitepaper provides an in-depth analysis of current methodologies, experimental protocols, and practical implementations for optimizing filtering and re-referencing procedures, specifically addressing the challenges posed by ocular contaminants. We synthesize evidence from recent studies to establish best practices for researchers, scientists, and drug development professionals working with EEG data in both experimental and clinical settings.

Ocular artifacts (OA), primarily generated by eye blinks and movements, represent the most pervasive non-neural contamination in electroencephalography (EEG) signals. These artifacts manifest as low-frequency, high-amplitude signals that predominantly affect frontal regions but can propagate across the scalp, significantly compromising signal quality and analysis reliability [25]. The electrooculogram (EOG) component of these artifacts arises from the corneo-retinal potential difference, which functions as a rotating electrical dipole with each eye movement. This introduces field potentials that overlap with neural signals of interest, particularly in the frequency range below 12 Hz [23].

The impact of ocular artifacts extends beyond simple signal contamination; they fundamentally alter the properties of EEG data in ways that can lead to erroneous conclusions in both basic research and clinical applications. For drug development professionals utilizing EEG as a biomarker, undetected or improperly handled ocular artifacts can confound treatment effect assessments, potentially leading to false positives or negatives in efficacy evaluations. The broader thesis context positions ocular artifacts not merely as technical nuisances but as significant confounding variables that must be addressed through optimized preprocessing pipelines to ensure the validity of neuroscientific inferences.

Filtering Strategies for Ocular Artifact Mitigation

Filtering constitutes the first line of defense against ocular artifacts in EEG preprocessing pipelines. Conventional approaches typically employ frequency-based filters to target the spectral characteristics of OAs, but recent advancements have introduced more sophisticated adaptive and model-based techniques that better preserve neural information while effectively removing artifacts.

Spectral Filtering Approaches

Traditional high-pass filtering with cutoff frequencies between 0.5-1 Hz effectively attenuates the slow drift components associated with ocular artifacts. However, over-aggressive high-pass filtering can distort genuine neural signals, particularly event-related potentials with low-frequency components. Evidence from systematic evaluations reveals that higher high-pass filter cutoffs (e.g., 1 Hz versus 0.1 Hz) consistently increase decoding performance across multiple experimental paradigms, though this may come at the cost of signal integrity [34]. Low-pass filtering with cutoffs around 30-40 Hz can further reduce higher-frequency artifacts that may co-occur with ocular events, but excessive attenuation can eliminate valuable neural information.

Table 1: Impact of Filter Choices on EEG Decoding Performance

Advanced and Adaptive Filtering Techniques

Recent innovations in filtering have moved beyond conventional frequency-based approaches to address the non-stationary nature of ocular artifacts. The Fixed Frequency Empirical Wavelet Transform (FF-EWT) integrated with a Generalized Moreau Envelope Total Variation (GMETV) filter has demonstrated particular efficacy for single-channel EEG systems, which present unique challenges for artifact removal [23]. This approach automatically identifies artifact-contaminated components using kurtosis, dispersion entropy, and power spectral density metrics, then applies targeted filtering to remove artifacts while preserving essential low-frequency EEG information.

For portable EEG systems increasingly used in healthcare applications, hybrid approaches combining convolutional neural networks (CNN) with least mean square (LMS) filtering have shown promising results [80]. In this architecture, CNN performs initial artifact removal through learned feature extraction, while the LMS filter provides adaptive refinement of the signal. Hardware-optimized implementations of this approach have achieved 77% reduction in area and 69.1% reduction in power consumption, making them suitable for wearable devices with limited computational resources [80].

Re-referencing Strategies to Minimize Ocular Artifact Propagation

Re-referencing procedures fundamentally reshape the spatial distribution of EEG signals and can either exacerbate or mitigate the impact of ocular artifacts. The choice of reference significantly influences signal topography and the apparent relationships between recording sites.

Common Re-referencing Approaches

The common average reference (CAR) subtracts the average potential across all electrodes from each individual channel, effectively creating a virtual reference at the spatial mean of the electrode array. While this approach can reduce widespread artifacts, it risks re-introducing ocular contamination when frontal channels with substantial OA contribute disproportionately to the average [81]. Robust statistical re-referencing procedures have been developed to address this limitation by down-weighting the influence of outlier channels, thereby reducing bias in low-density EEG setups [82] [83].

The reference electrode standardization technique (REST) leverages physical principles to estimate signals against an infinite reference, potentially providing more accurate source localization. However, REST typically requires dense electrode sampling and knowledge of electrode locations, making it less practical for low-density clinical setups [82]. For studies specifically concerned with ocular artifacts, mastoid or linked-ear references may offer advantages, as these sites are relatively distant from ocular sources, though they can introduce their own biases if the reference sites themselves become contaminated.

Impact on Data Integrity and Analysis

Re-referencing choices profoundly affect subsequent analyses, particularly those examining functional connectivity or source localization. Improper re-referencing can introduce spurious correlations between channels that may be misinterpreted as functional networks [82]. The robust re-referencing procedure introduced by Lepage et al. demonstrates that statistically-informed approaches can reduce these artifactual correlations while maintaining unbiased estimation across diverse recording scenarios [83].

Table 2: Comparison of Re-referencing Methods for Ocular Artifact Mitigation

Experimental Protocols and Methodological Considerations

Evaluating Preprocessing Efficacy

Systematic evaluation of preprocessing pipelines requires standardized protocols and validation metrics. The multiverse approach, which systematically varies preprocessing steps across a grid of possible parameter combinations, has emerged as a robust methodology for assessing the impact of preprocessing choices on analytical outcomes [34]. This approach typically involves:

• Defining preprocessing dimensions: Key parameters including filter cutoffs, re-referencing methods, artifact correction techniques, and baseline correction intervals are identified as dimensions for exploration.

• Generating preprocessing pipelines: All possible combinations of parameter settings are systematically assembled into distinct preprocessing pipelines.

• Applying pipelines to benchmark datasets: Each pipeline is applied to standardized datasets with known properties, such as the ERP CORE dataset containing multiple event-related potential paradigms [34].

• Quantifying outcomes: Downstream analyses including decoding performance, effect size estimation, and source localization accuracy are computed for each pipeline.

• Comparative analysis: The impact of specific preprocessing choices is quantified through statistical models that isolate the contribution of each parameter while marginalizing out the effects of others.

Protocol for Targeted Ocular Artifact Reduction

The RELAX pipeline implements a targeted artifact reduction method that specifically addresses limitations of conventional approaches like independent component analysis (ICA) [84]. The protocol involves:

• ICA decomposition: EEG data is decomposed into independent components using extended infomax ICA.

• Component classification: Neural network-based classifiers (e.g., ICLabel) identify components dominated by ocular activity.

• Targeted correction: Rather than completely removing artifactual components, this approach:

  • Identifies periods of actual eye movements within ocular components
  • For muscle artifacts, targets only artifact-dominated frequency bands
  • Reconstructs data while preserving neural information in non-artifact periods and frequencies

• Validation: Processed data is evaluated for artifact reduction effectiveness and neural signal preservation using metrics like signal-to-artifact ratio and effect size inflation factors.

Impact on Analytical Outcomes and Decoding Performance

The interaction between preprocessing choices and analytical outcomes is particularly evident in decoding performance, where artifact handling strategies can significantly influence classification accuracy and interpretability.

Empirical Evidence from Systematic Studies

Comprehensive evaluations reveal that artifact correction steps generally decrease decoding performance across experiments and models [34]. For instance, removing ocular artifacts via ICA was strongly negatively associated with decoding performance in the N2pc experiment, where eye movements are systematically associated with the target position and thus predictive for the decoder [34]. Similarly, removing muscle artifacts negatively impacted decoding performance in the lateralized readiness potential experiment where hand movements were decoded [34].

These findings underscore a critical consideration: when artifacts are systematically correlated with experimental conditions, their removal may eliminate valid predictive information. However, retaining such artifacts jeopardizes the interpretability and neurological validity of decoding models, as they may exploit structured noise rather than neural signals [34].

Effect Size Inflation and Source Localization Biases

Conventional artifact removal approaches can artificially inflate effect sizes and bias source localization estimates. Bailey et al. demonstrated that subtracting artifactual ICA components indiscriminately removes both neural and non-neural signals, leading to inflated event-related potential and connectivity effect sizes [84]. Their targeted artifact reduction method effectively cleaned artifacts while minimizing these biases, enhancing the reliability and validity of EEG analyses.

Implementation Guidelines and Best Practices

Based on current evidence, we recommend the following best practices for filtering and re-referencing within EEG preprocessing pipelines concerned with ocular artifacts:

• Adopt a tiered filtering approach: Implement conservative high-pass filtering (0.1-0.5 Hz) for initial artifact attenuation, followed by more targeted methods like FF-EWT+GMETV or CNN-LMS hybrids for residual artifact removal [23] [80].

• Select re-referencing strategically: For low-density EEG setups, robust re-referencing procedures that minimize the influence of outlier channels are preferable to common average reference [82]. For high-density setups with known electrode locations, REST may offer advantages.

• Validate pipeline efficacy: Use a multiverse approach to quantify the impact of preprocessing choices on specific analytical outcomes relevant to your research questions [34].

• Prioritize targeted correction: Instead of complete artifact removal, implement targeted approaches that selectively correct artifact-dominated periods or frequencies while preserving neural information [84].

• Balance performance and interpretability: When using decoding approaches, recognize that maximizing classification accuracy may come at the cost of neurological interpretability if artifacts are leveraged as predictive features [18].

Table 3: Research Reagent Solutions for EEG Preprocessing

Optimizing filtering and re-referencing procedures is essential for mitigating the confounding effects of ocular artifacts in EEG research. The current evidence demonstrates that these preprocessing steps significantly influence downstream analytical outcomes, including decoding performance, effect size estimation, and source localization. Rather than seeking universal solutions, researchers should implement context-aware preprocessing strategies that balance artifact removal with neural signal preservation. The development of targeted correction approaches, robust statistical methods, and hardware-optimized algorithms represents significant advances in addressing the perennial challenge of ocular artifacts. For the broader thesis on how ocular artifacts affect EEG data analysis, these preprocessing optimizations form the critical technical foundation for ensuring analytical validity and neurological interpretability.

Diagrams

Preprocessing Pipeline for Ocular Artifact Mitigation

Targeted Artifact Removal Workflow

Impact of Preprocessing on Decoding Performance

In electroencephalography (EEG) research, the integrity of neural data is paramount. Ocular artifacts, generated by blinks and eye movements, represent one of the most pervasive challenges in EEG signal analysis. These artifacts originate from the corneo-retinal dipole—the positive charge of the cornea relative to the retina—which creates substantial electrical potentials that propagate across the scalp [1] [21]. During blinks and saccades, the movement of eyelids and rotation of eyeballs generate electrical signals that can overwhelm genuine neural activity, particularly in frontal brain regions [1]. The amplitude of ocular artifacts often reaches hundreds of microvolts, dramatically exceeding the typical 10-100 μV range of endogenous EEG rhythms [1]. This contamination extends beyond simple signal obstruction; it introduces systematic biases that can fundamentally alter research conclusions, particularly in studies of cognition, perception, and drug effects where precise neural timing and spectral content are critical analytical variables.

The interference of ocular artifacts spans both temporal and spectral domains. In time-domain analyses, such as event-related potential (ERP) studies, blinks produce high-amplitude deflections that can obscure or mimic cognitive components like the P300 or N400 [18] [1]. In frequency-domain analyses, the spectral profile of ocular artifacts (3-15 Hz) directly overlaps with clinically and cognitively relevant EEG bands including delta, theta, and alpha rhythms [1]. This spectral overlap creates particular challenges for research investigating drug-induced changes in neural oscillations or resting-state brain activity. Without appropriate artifact management, studies risk conflating pharmacological effects with artifact-induced signal variations, potentially leading to erroneous conclusions in clinical trials and basic neuroscience research.

Quantitative Comparison of Artifact Management Strategies

Table 1: Performance Comparison of Primary Artifact Management Techniques

Table 2: Context-Specific Recommendations for Artifact Management

The decision between artifact correction and rejection involves careful consideration of research objectives, data characteristics, and analytical requirements. Recent evidence suggests that for many experimental paradigms, correction approaches alone may be sufficient, with rejection providing minimal additional benefit for decoding performance [18]. A comprehensive evaluation across seven common ERP paradigms found that combining artifact correction with rejection did not significantly enhance decoding performance in most cases [18]. This finding challenges conventional practices of aggressive trial rejection, particularly valuable for studies with limited trial counts or within-subject designs.

The choice of correction method must align with both data acquisition parameters and research goals. Methods like ICA demonstrate excellent performance with high-density systems but become less effective with low-channel-count setups typical in clinical or mobile settings [38] [1]. Conversely, emerging deep learning approaches like EEGOAR-Net show promise for cross-subject and cross-montage applications without requiring reference EOG channels [25]. For online analysis or brain-computer interface applications, methods like Artifact Subspace Reconstruction (ASR) and online Empirical Mode Decomposition (EMD) have demonstrated sensitivity comparable to offline processing while enabling real-time implementation [85].

Methodological Protocols for Artifact Management

Independent Component Analysis (ICA) Workflow

ICA has established itself as a reference method for ocular artifact correction, particularly in high-density EEG systems. The protocol involves several critical steps:

• Data Preparation: Begin by applying a high-pass filter (typically 1-2 Hz cutoff) to remove slow drifts that can impair ICA decomposition. This step is crucial for stabilizing the baseline and improving component estimation [1].

• ICA Decomposition: The algorithm separates the multichannel EEG data into maximally independent components based on their statistical properties. Each component possesses a fixed scalp topography and an associated time course of activation [44].

• Component Identification: Ocular components are identified through their characteristic features: (1) strong frontal topography with polarity reversals between frontal and posterior regions, (2) time courses showing high-amplitude, brief bursts corresponding to blinks or saccades, and (3) spectral profiles dominated by low-frequency content [44] [21]. Visualization tools component scalp maps, activity time courses, and power spectra facilitate this identification.

• Component Removal: Once ocular components are identified, they are projected out of the data, effectively removing their contribution while preserving neural activity from other components [44].

Artifact Subspace Reconstruction (ASR) Protocol

ASR represents an adaptive, automated approach particularly suited for online processing and mobile EEG applications:

• Calibration Phase: A segment of clean, artifact-free EEG data is used to establish baseline statistics for the covariance structure of neural signals. This calibration data can be drawn from resting-state periods or artifact-free intervals within the recording [85].

• Sliding Window Processing: The algorithm processes EEG data using a sliding window (typically 0.5-1 second durations). For each window, the covariance structure is compared against the calibrated baseline [85] [86].

• Artifact Subspace Identification: Windows exhibiting covariance structures significantly deviating from the calibrated baseline are flagged as containing artifacts. The method identifies the multidimensional subspace where these deviations occur [85].

• Reconstruction: Artifactual subspaces are reconstructed using the baseline statistics through principal component analysis, effectively removing artifact contributions while preserving neural activity that conforms to the calibrated baseline [85] [86].

Regression-Based Methods

Traditional regression approaches remain viable, particularly when EOG recordings are available:

• Calibration Phase: Participants complete a calibration run where spontaneous blinks and eye movements are recorded simultaneously with EEG and EOG. This establishes subject-specific propagation coefficients (β) quantifying how ocular potentials spread to each EEG channel [1].

• Propagation Modeling: The relationship is modeled as: RawEEGₑᵢ(n) = EEGₑᵢ(n) + βₑᵢ × Artifacts(n) where βₑᵢ represents the channel-specific weight of ocular interference [1].

• Artifact Subtraction: During the correction phase, the EOG component weighted by the estimated β coefficients is subtracted from each EEG channel, removing the modeled ocular influence [1].

Decision Framework for Artifact Management

The choice between correction and rejection strategies depends on multiple factors including research objectives, data characteristics, and analytical requirements. The following decision framework provides a systematic approach for selecting appropriate artifact management strategies:

This decision framework emphasizes that correction should generally be prioritized over rejection, particularly when trial counts are limited or when analyses require complete trial preservation [18]. Rejection remains appropriate for severe, non-ocular artifacts (e.g., muscle bursts, electrode pops) that cannot be adequately corrected without introducing significant distortion [21]. For standard analysis pipelines, implementing correction followed by minimal rejection of residual artifacts represents the most balanced approach.

Table 3: Research Reagent Solutions for Advanced Artifact Management

Effective management of ocular artifacts requires a nuanced approach that balances signal preservation with artifact removal. The evidence increasingly supports correction-focused strategies over wholesale trial rejection, particularly for studies where statistical power is limited or where trial-to-trial variability carries important information [18]. While traditional methods like ICA and regression remain valuable for specific applications, emerging approaches including ASR and deep learning offer powerful alternatives for real-time applications and low-channel-count environments [85] [25].

Critically, artifact management must be guided by the specific research context rather than one-size-fits-all pipelines. For drug development studies investigating subtle neurophysiological effects, preservation of trial counts through effective correction may be essential for detecting treatment effects. Conversely, for simple binary classification paradigms, minimal preprocessing may suffice without significant performance penalties [18]. Regardless of the specific methods chosen, transparent reporting of artifact management strategies is essential for research reproducibility and interpretation—particularly when studying populations with elevated artifact prevalence or when employing novel analytical approaches where artifact impacts may not be fully characterized.

As EEG technologies continue to evolve toward mobile, real-world applications, artifact management strategies must similarly advance to address the unique challenges of uncontrolled environments. The integration of auxiliary sensors, adaptive algorithms, and artifact-resistant experimental designs will further enhance our capacity to extract meaningful neural signals despite the persistent challenge of ocular artifacts. Through strategic implementation of evidence-based artifact management approaches, researchers can maximize both data quality and analytical power across diverse experimental paradigms.

Validating Correction Efficacy: Performance Metrics and Comparative Method Analysis

In electroencephalography (EEG) research, the pervasive presence of ocular artifacts represents a fundamental challenge to data integrity and interpretation. These artifacts, primarily originating from eye blinks and movements, introduce substantial noise that can obscure genuine neural signals and confound analytical outcomes. This technical guide provides an in-depth examination of three critical performance metrics—Mean Squared Error (MSE), Correlation Coefficient (CC), and Signal-to-Artifact Ratio (SAR)—for quantifying the efficacy of ocular artifact removal pipelines. Framed within the context of a broader thesis on how ocular artifacts affect EEG data analysis, this paper details experimental protocols for benchmark validation, presents performance data from state-of-the-art deep learning models, and offers a standardized toolkit for researchers to evaluate and compare artifact correction methodologies. The systematic application of these metrics is paramount for advancing the reliability of EEG in clinical diagnostics, neuroscientific discovery, and drug development.

Electroencephalography (EEG) is a cornerstone technique in neuroscience and clinical diagnostics, prized for its non-invasiveness and millisecond-scale temporal resolution. However, its utility is perpetually challenged by biological artifacts, with ocular artifacts (OAs)—generated by eye blinks (electrooculographic, EOG) and movements—being among the most prevalent and disruptive [73]. OAs manifest as high-amplitude, low-frequency signals that can mask genuine neural activity and lead to erroneous conclusions in both conventional univariate and advanced multivariate pattern analyses [9] [18].

The central thesis of this broader work posits that ocular artifacts systematically bias EEG data analysis, potentially altering key event-related potential (ERP) components, distorting power spectral density estimates, and ultimately compromising the validity of neuroscientific and clinical findings. Consequently, robust artifact removal strategies are not merely a preprocessing step but a critical determinant of data quality. Evaluating the success of these strategies demands a multifaceted approach, employing quantitative metrics that assess different dimensions of performance. This guide focuses on three such metrics:

• Mean Squared Error (MSE): Quantifies the absolute deviation of the cleaned signal from a ground truth.
• Correlation Coefficient (CC): Assesses the morphological similarity and preservation of neural dynamics.
• Signal-to-Artifact Ratio (SAR): Measures the success in suppressing artifactual power.

By detailing their calculation, interpretation, and application within standardized experimental protocols, this guide aims to equip researchers with the necessary tools to critically assess and advance the field of OA remediation.

Metric Definitions and Interpretations

Mean Squared Error (MSE) and Root Mean Squared Error (RMSE)

MSE is a fundamental metric that measures the average squared difference between the cleaned or estimated signal and the true, artifact-free ground truth [88] [89]. It is defined as:

[ \text{MSE} = \frac{1}{n}\sum{i=1}^{n}(Yi - \hat{Y}_i)^2 ]

where (Yi) is the actual (ground truth) value, (\hat{Y}i) is the predicted (cleaned) value, and (n) is the number of observations [88] [89] [90]. The squaring of errors ensures that MSE is sensitive to large deviations, heavily penalizing outliers and significant artifacts that remain in the signal [91]. A value of zero indicates a perfect reconstruction, with increasing values signifying greater error.

The Root Mean Squared Error (RMSE) is derived as the square root of the MSE ((\text{RMSE} = \sqrt{\text{MSE}})) [89] [90]. This transformation returns the metric to the original units of the data, enhancing its interpretability. For an unbiased estimator, the RMSE is equivalent to the standard error [88]. In the context of ocular artifact removal, a lower MSE/RMSE indicates a cleaned signal that more closely approximates the true neural data.

Table 1: Interpretation of MSE and RMSE in EEG Artifact Removal

Correlation Coefficient (CC)

The Correlation Coefficient (CC), most often Pearson's (r), quantifies the strength and direction of the linear relationship between the cleaned EEG signal and the ground truth [92]. It is calculated as:

[ r = \frac{\sum{i=1}^{n}(Yi - \bar{Y})(\hat{Y}i - \bar{\hat{Y}})}{\sqrt{\sum{i=1}^{n}(Yi - \bar{Y})^2}\sqrt{\sum{i=1}^{n}(\hat{Y}_i - \bar{\hat{Y}})^2}} ]

The value of CC ranges from -1 to +1. A value of +1 denotes a perfect positive linear relationship, 0 indicates no linear relationship, and -1 signifies a perfect negative linear relationship [92]. In artifact removal, a CC close to +1 is desired, as it indicates that the temporal dynamics and morphology of the underlying neural signal have been preserved in the cleaned output.

There is no universal consensus on naming the strength of correlation coefficients, and interpretations can vary by research domain. However, guidelines from medical literature suggest that a value above 0.7 can be considered "strong," between 0.3 and 0.7 "moderate," and below 0.3 "weak" [92].

Signal-to-Artifact Ratio (SAR)

The Signal-to-Artifact Ratio (SAR) is a metric specifically designed to evaluate the effectiveness of artifact removal algorithms. It measures the ratio of the power of the desired neural signal to the power of the residual artifact remaining after processing. A higher SAR indicates more effective suppression of the artifact and better preservation of the neural signal. While a universal formula is less standardized than for MSE or CC, it is conceptually calculated by comparing the power of the signal before and after artifact removal in specific frequency bands, or by leveraging ground-truth data in semi-simulated experiments. Its direct focus on the artifact component makes it an indispensable complement to MSE and CC.

Experimental Protocols for Metric Validation

Rigorous validation of artifact removal techniques requires carefully designed experiments that enable the calculation of the aforementioned metrics. The following protocols are standard in the field.

The Semi-Synthetic Benchmark Experiment

This protocol is the most common for obtaining a reliable ground truth for metric calculation.

• Acquisition of Clean EEG: Record EEG data during conditions that minimize artifacts (e.g., resting state with minimized eye blinks) or use pre-validated, clean EEG segments from public databases [33] [73].
• Acquisition of Ocular Artifacts: Record pure EOG signals, typically from dedicated electrodes around the eyes.
• Linear Mixing: Artificially contaminate the clean EEG segments by adding the recorded EOG signals at a known, controlled amplitude. This generates a dataset with artifact-contaminated signals ((EEG{contaminated})) and a known ground truth ((EEG{clean})) [33].
• Algorithm Application & Evaluation: Apply the artifact removal algorithm (e.g., a deep learning model) to (EEG{contaminated}) to produce (EEG{cleaned}). Then, compute MSE, CC, and SAR by comparing (EEG{cleaned}) to (EEG{clean}).

Real-Data Validation with Expert Annotation

For fully real-world data where a perfect ground truth is unavailable, a proxy validation is used.

• Data Acquisition: Collect multi-channel EEG data from subjects performing tasks that naturally induce ocular artifacts (e.g., a 2-back working memory task) [33].
• Expert Identification: Have experienced electrophysiologists visually inspect the data and identify segments heavily contaminated by ocular artifacts.
• Algorithm Application: Process the identified contaminated segments through the removal pipeline.
• Qualitative & Indirect Quantitative Evaluation: Experts then assess the cleaned segments for the removal of artifactual patterns and the plausible retention of neural signals. SAR can be estimated by comparing power in artifact-dominated frequency bands (e.g., low frequencies for blinks) before and after cleaning.

Performance of State-of-the-Art Models

Recent advances in deep learning have produced several models that explicitly report performance using MSE, CC, and SAR on ocular artifact removal tasks. The following table synthesizes results from recent studies on semi-synthetic benchmarks.

Table 2: Performance Comparison of Deep Learning Models on Ocular Artifact Removal

The experimental workflow for developing and benchmarking these models follows a structured pipeline, as illustrated below.

Diagram 1: Experimental Workflow for Benchmarking Artifact Removal

The Scientist's Toolkit: Research Reagents & Materials

The following table details essential "research reagents"—the key algorithms, data, and software—required for experiments in this field.

Table 3: Essential Research Reagents for EEG Artifact Research

The logical relationship between the core metrics, the artifact removal process, and the ultimate goal of clean data analysis is summarized in the following diagram.

Diagram 2: Logic Model Linking Metrics to Research Success

Ocular artifacts present a formidable challenge in EEG data analysis, with the potential to skew research findings and clinical interpretations. This guide has established a framework for defining and measuring success in overcoming this challenge through three key performance metrics: MSE (and RMSE) for quantifying fidelity, CC for assessing signal preservation, and SAR for directly measuring artifact suppression. The provided experimental protocols and performance benchmarks from cutting-edge deep learning models like CLEnet and AnEEG offer a standardized pathway for validation. As the field progresses toward more automated and robust solutions, the consistent and comprehensive application of these metrics will be indispensable for researchers, scientists, and drug development professionals aiming to ensure the highest standards of data quality and analytical rigor in EEG research.

Electroencephalography (EEG) is a fundamental tool in neuroscience research and clinical drug development, providing direct measurement of neuronal electrical activity with millisecond temporal resolution. However, the utility of EEG data is frequently compromised by ocular artifacts (OAs), particularly those generated by eye blinks and movements. These artifacts manifest as high-amplitude, low-frequency signals that can obscure or mimic genuine neural activity, potentially leading to erroneous interpretations of brain function and treatment effects [23] [25].

The amplitude of ocular artifacts can be an order of magnitude greater than that of cortical EEG signals, dominating the recorded signal and significantly reducing the signal-to-noise ratio. This is especially problematic in event-related potential (ERP) studies where the artifactual components can overlap with key cognitive components like P300 or N400, thereby threatening the validity of both conventional univariate analyses and modern multivariate pattern analysis (MVPA) for decoding brain states [9] [18]. Effectively addressing these artifacts is therefore not merely a technical preprocessing step but a fundamental prerequisite for ensuring data integrity in neuroscientific and pharmaco-EEG research.

This whitepaper provides a comprehensive technical comparison of four dominant methodologies for ocular artifact correction: Independent Component Analysis (ICA), Regression-based techniques, Artifact Subspace Reconstruction (ASR), and Deep Learning (DL) approaches. We evaluate their underlying principles, implementation protocols, performance metrics, and suitability for various research contexts, with a particular focus on their impact within the rigorous framework of clinical and translational research.

Methodological Fundamentals and Experimental Protocols

This section delineates the core principles, experimental workflows, and specific protocols for each artifact correction method, providing a foundation for their comparative analysis.

Independent Component Analysis (ICA)

Principle: ICA is a blind source separation technique that decomposes multi-channel EEG data into statistically independent components (ICs). It operates on the principle that mixed signals, like EEG, are linear combinations of independent source signals, including neural and artifactual sources [23]. The goal is to unmix these sources so that individual components represent either cerebral activity or distinct artifacts.

Experimental Protocol: The standard protocol for ICA-based artifact removal is methodical and requires several steps, typically implemented in tools like EEGLAB. The workflow is linear and iterative, as shown in Figure 1.

Figure 1: ICA-based artifact removal workflow.

A critical, manually intensive step is component identification. Artifactual components related to eye blinks are typically identified by their:

• Topography: Featuring strong frontal projections.
• Time-course: Showing high amplitude, stereotypical waveforms coinciding with blinks.
• Spectral properties: Dominated by low-frequency content [9] [18].

Regression-Based Methods

Principle: Regression techniques model the relationship between EEG channels and simultaneously recorded electrooculogram (EOG) channels. The artifact contribution to each EEG channel is estimated as a weighted linear combination of the EOG signals and then subtracted from the contaminated EEG [25]. This method relies on the availability of dedicated EOG reference channels.

Experimental Protocol: The regression protocol is mathematically straightforward but depends heavily on the quality of the EOG reference. The logical relationship of the process is outlined in Figure 2.

Figure 2: Regression-based artifact correction workflow.

Artifact Subspace Reconstruction (ASR)

Principle: ASR is an online-capable, data-driven method that functions by identifying and removing high-variance signal components that exceed a statistically defined threshold relative to a clean baseline ("calibration") data segment. It operates on the principle that large-amplitude artifacts occupy a specific subspace within the high-dimensional EEG signal space, which can be identified and reconstructed using surrounding clean data [23].

Deep Learning (DL) Models

Principle: Deep learning approaches, particularly those based on U-Net architectures, learn a non-linear mapping from artifact-contaminated EEG signals to their clean counterparts. These are end-to-end models that bypass the need for manual component selection or explicit reference signals after training on large datasets of paired (contaminated and clean) EEG data [25].

Experimental Protocol: The protocol for DL-based correction is divided into a training phase and an application phase, with the latter being fully automated. The process for a state-of-the-art model like EEGOAR-Net is visualized in Figure 3.

Figure 3: Deep learning-based artifact correction workflow.

A key advancement is montage-independent models like EEGOAR-Net, which use a novel training methodology involving channel masking to generalize across different EEG cap layouts without requiring retraining [25].

Quantitative Performance Comparison

The following tables synthesize quantitative and qualitative findings from recent studies to facilitate a direct comparison of the four methods.

Table 1: Core Algorithmic Characteristics and Resource Requirements

Table 2: Performance Comparison on Key Metrics

A critical finding from recent research is that while artifact correction is essential to prevent artificially inflated decoding accuracies, the combination of ICA correction and artifact rejection did not significantly enhance the decoding performance of Support Vector Machines (SVMs) and Linear Discriminant Analysis (LDA) in the vast majority of cases across a wide range of ERP paradigms [9] [18]. This suggests that the primary benefit of correction may be in preventing confounds rather than boosting raw decoding power.

The Scientist's Toolkit: Essential Research Reagents and Materials

For researchers aiming to implement and validate these artifact correction methods, particularly in a preclinical or clinical trial context, the following toolkit is essential.

Table 3: Essential Research Materials and Reagents for Ocular Artifact Research

The choice of an optimal ocular artifact correction strategy is not one-size-fits-all but must be aligned with the specific research goals, experimental constraints, and analysis pipelines. ICA remains a powerful, widely accepted standard for offline analysis where manual inspection is feasible. Regression-based methods offer a simple solution when reliable EOG recordings are available, albeit with risks of neural signal loss. ASR provides a robust, automated option for online processing and cleaning of continuous data. Deep Learning represents the frontier, offering the promise of fully automated, calibration-free, and highly effective correction that generalizes across subjects and montages.

For research in drug development, where throughput, reproducibility, and the integrity of neural markers are paramount, the trend is moving toward fully automated pipelines. In this context, deep learning-based methods like EEGOAR-Net, which require no subject-specific calibration or additional EOG channels, present a significant advantage for standardizing analyses across multi-site clinical trials [25]. Regardless of the method chosen, this comparative analysis underscores that rigorous handling of ocular artifacts is not an optional preprocessing step but a foundational element of valid and reliable EEG research.

Ocular artifacts represent a pervasive challenge in electroencephalography (EEG) research, introducing significant noise that can compromise data integrity and interpretation. These artifacts, generated by eye blinks and movements, produce electrical signals that contaminate the neural data recorded from the scalp [21]. The corneal-retinal potential difference—with the positively charged cornea and negatively charged retina—creates a dipole that rotates during eye movements, generating electrical currents that spread across the scalp [21]. This contamination is particularly prominent in frontal regions but extends to posterior sites, affecting virtually all electrodes to varying degrees. The impact of these artifacts extends beyond simple signal quality issues to fundamentally influence downstream analytical outcomes, including event-related potential (ERP) morphology assessment and emerging multivariate pattern analysis (MVPA) approaches. Understanding these dual impacts is crucial for researchers conducting EEG studies, particularly in drug development and clinical populations where ocular artifacts may be more prevalent or systematic.

This technical guide examines the complex relationship between ocular artifacts, ERP morphology, and multivariate decoding performance, synthesizing current evidence to provide methodological recommendations for researchers navigating these analytical challenges.

Ocular Artifacts: Mechanisms and Characteristics

Physiological Origins and Electrical Properties

The primary mechanism underlying ocular artifacts stems from the corneo-retinal dipole of the eye, which creates a consistent electrical field. During blinks, the eyelid slides over this dipole, inverting polarity and creating a positive current toward the scalp that manifests as a high-amplitude, transient deflection in EEG recordings [21]. During lateral eye movements, the dipole rotates toward the temples, producing characteristic box-shaped deflections with opposite polarities on opposite sides of the head [21].

The table below summarizes the key characteristics of major ocular artifact types:

Table 1: Characteristics of Ocular Artifacts

Impact on ERP Morphology and Measurement

Ocular artifacts introduce two primary problems for ERP analysis: they create confounds when systematically differing across experimental conditions, and they increase uncontrolled variance that reduces statistical power [93]. When participants blink more in one condition than another, EOG voltage can create artificial differences misinterpreted as neural effects [93]. Even random artifacts add noise that can obscure true effects, potentially leading to Type I or Type II errors in statistical analysis.

The temporal and spatial characteristics of ocular artifacts directly compete with neural signals of interest. Blink artifacts typically last 200-400 ms [11], potentially overlapping entirely with early and middle-latency ERP components. Their frontal maximum distribution particularly affects components like the error-related negativity (ERN) and feedback-related negativity (FRN), while their spectral concentration in delta and theta bands interferes with cognitive components like P300 and N400 [21].

Methodological Approaches for Artifact Management

Artifact Correction Techniques

Multiple approaches exist for addressing ocular artifacts, each with distinct mechanisms and applications:

Table 2: Ocular Artifact Correction Methods

Electrode Selection Considerations

The traditional approach for EOG measurement involves facial electrodes placed above, below, and to the outside of the eyes. However, recent evidence suggests that cap-based electrodes (e.g., Fp1, Fp2, FT9, FT10) can provide viable alternatives without requiring facial electrodes [94]. Studies comparing these approaches found comparable split-half reliability and standardized measurement error between facial and cap electrode approaches for components like the reward positivity (RewP) and late positive potential (LPP) [94]. This option is particularly valuable for populations where facial electrodes present challenges (e.g., sensory sensitivities, young children).

Impact on Multivariate Decoding Performance

Complex Relationship Between Artifacts and Decoding

The impact of ocular artifact correction on multivariate pattern analysis (MVPA) presents a more complex picture than its effect on traditional ERP analysis. Surprisingly, systematic investigations have revealed that artifact correction steps often reduce decoding performance across multiple experimental paradigms [18] [34].

A comprehensive study evaluating artifact correction and rejection on support vector machine (SVM) and linear discriminant analysis (LDA) decoding performance found that "the combination of artifact correction and rejection did not improve decoding performance in the vast majority of cases" [18]. Similarly, a multiverse analysis of seven EEG experiments demonstrated that both ICA-based ocular correction and automated artifact rejection consistently decreased decoding accuracy for both neural network (EEGNet) and time-resolved logistic regression classifiers [34].

Explanatory Mechanisms

This counterintuitive relationship emerges because ocular artifacts can be systematically correlated with experimental conditions. For instance, in visual attention paradigms requiring lateralized processing, participants make systematic eye movements toward attended stimuli. When these artifacts are removed, the decoder loses predictive features that were contamination-derived rather than neural in origin [34].

This creates a fundamental tension: leaving artifacts uncorrected may artificially inflate decoding performance by allowing models to exploit systematic non-neural signals, compromising interpretability and validity. As Zhang et al. caution, "artifact correction may still be essential to minimize artifact-related confounds that might artificially inflate decoding accuracy" [18].

Experimental Protocols and Assessment Framework

Standardized Assessment Methodology

To evaluate the effectiveness of artifact minimization approaches, researchers can implement the following protocol adapted from current methodological research:

• Data Collection: Utilize standardized paradigms that generate well-established ERP components (e.g., ERP CORE battery including P3b, N400, N170, MMN, ERN) [93]

• Artifact Manipulation: Apply multiple artifact handling approaches (e.g., no correction, ICA-only, rejection-only, combined correction-rejection) to the same dataset

• Confound Assessment: Compare eyeblink rates and amplitudes across experimental conditions to identify potential systematic differences

• Data Quality Metrics: Calculate standardized measurement error (SME) and split-half reliability for each approach [94] [93]

• Decoding Performance Evaluation: Train and test classifiers using identical procedures across artifact handling conditions, reporting both accuracy and potential confounds

Quantitative Outcomes

Research applying this framework has yielded several key findings:

Table 3: Quantitative Outcomes of Artifact Correction on ERP and Decoding Measures

Integrated Analytical Workflow

The diagram below illustrates the decision pathway for managing ocular artifacts based on analytical priorities:

The Scientist's Toolkit: Research Reagent Solutions

Table 4: Essential Resources for Ocular Artifact Management in EEG Research

Discussion and Future Directions

The relationship between ocular artifacts and downstream EEG analysis reveals a fundamental trade-off between signal purity and analytical performance. While artifact correction consistently benefits traditional ERP analysis by reducing confounds and improving measurement precision [94] [93], its value for multivariate decoding is more nuanced. The observed decrease in decoding performance after artifact correction [18] [34] suggests that current correction approaches may remove diagnostically useful variance, or that decoders inadvertently leverage systematic artifacts.

This tension necessitates careful consideration of research goals when designing artifact management strategies. For confirmatory studies seeking valid neural correlates, comprehensive artifact correction remains essential despite potential performance costs. For brain-computer interface applications maximizing accuracy, limited correction with careful interpretation may be preferable.

Future methodological development should focus on techniques that distinguish neural from artifactual signals while preserving condition-relevant information. Advanced approaches might include artifact-invariant feature learning or multi-task architectures that simultaneously predict experimental conditions and artifact presence. Furthermore, the field would benefit from standardized reporting of artifact prevalence and handling procedures, enabling better cross-study comparison and interpretation of decoding results.

Ocular artifacts present a dual challenge for EEG researchers, affecting both ERP morphology and multivariate decoding performance through distinct mechanisms. While established correction methods effectively address artifacts for univariate ERP analysis, their application to multivariate decoding requires more careful consideration due to the risk of removing systematically predictive variance. Researchers must balance the competing demands of signal purity and analytical performance, selecting artifact management strategies that align with their specific research goals and interpretive frameworks. By implementing rigorous assessment protocols and transparent reporting practices, the field can advance toward more robust and interpretable EEG analysis in the presence of these ubiquitous artifacts.

Electroencephalography (EEG) provides a non-invasive window into brain function with millisecond temporal resolution, making it invaluable for neuroscience research and clinical applications ranging from brain-computer interfaces to drug development [35] [96]. However, the electroencephalographic signal's fidelity is consistently compromised by ocular artifacts—electrical potentials generated by eye movements and blinks that can overwhelm neural activity [35]. These artifacts present researchers with a persistent dilemma: how to remove contaminating signals without sacrificing genuine neural information essential for valid conclusions.

Ocular artifacts pose particularly significant challenges due to three fundamental properties: their spectral bandwidth (3-15 Hz) overlaps informatively with EEG theta and alpha rhythms; they occur with high frequency (12-18 blinks per minute); and they exhibit much larger amplitudes than background neural signals [35]. In pharmacological and clinical trials, where EEG often serves as a primary biomarker for drug effects, improper artifact handling can distort pharmacokinetic-pharmacodynamic relationships and lead to erroneous conclusions about treatment efficacy [97]. This technical guide examines the information loss trade-off inherent in ocular artifact removal methods, providing researchers with evidence-based strategies for preserving neural integrity throughout the signal processing pipeline.

Neurophysiological Origins and Technical Characteristics of Ocular Artifacts

Biological Mechanisms of Ocular Artifact Generation

Ocular artifacts originate from three primary physiological sources that contaminate EEG recordings through distinct mechanisms. The corneo-retinal dipole establishes a positive charge at the cornea relative to the retina, creating an electrical field that rotates with eyeball movement and generates potential changes at EEG electrodes [35]. Eyelid movements during blinks introduce high-amplitude potential field changes as the eyelid slides across the corneal surface. Additionally, extraocular muscle contractions during eye movements produce electromyographic signals that affect EEG signal amplitude [35]. These combined sources create artifact potentials that can be an order of magnitude larger than cortical signals, particularly affecting frontal and prefrontal electrode sites nearest the eyes.

Spectral and Temporal Characteristics with Clinical Impact

The table below summarizes the key characteristics of ocular artifacts that complicate their removal:

Table 1: Characteristics and Research Impacts of Ocular Artifacts

The spectral overlap presents perhaps the most significant technical challenge, as conventional filtering approaches inevitably remove neural signals along with artifacts. In pharmaco-EEG studies, this overlap has been shown to meaningfully impact conclusions about drug effects on brain function, particularly for compounds affecting alpha and theta rhythms [97].

Methodological Approaches: Trade-offs Between Preservation and Removal

Traditional Signal Processing Methods

Regression-Based Techniques

Regression methods represent the earliest systematic approach to ocular artifact correction, operating on the principle that recorded EEG represents a linear combination of neural signals and ocular artifacts [35]. The fundamental equation models the recorded signal as:

RawEEG(n) = EEG(n) + artifacts(n) [35]

The Gratton and Cole algorithm implementation follows a standardized processing chain: band-pass filtering (1-50 Hz) of raw EEG to remove slow fluctuations and high-frequency noise, low-pass filtering (15 Hz cutoff) of EOG signals to eliminate high-frequency components, and subtraction of weighted EOG templates from each EEG channel using subject-specific propagation factors [35]. While computationally efficient, regression approaches suffer from a critical limitation: they operate under the false assumption that EOG channels contain "pure" ocular signals, when in fact these references also include cerebral activity, leading to the unnecessary removal of neural information [97].

Blind Source Separation and Independent Component Analysis

Blind Source Separation (BSS) methods, particularly Independent Component Analysis (ICA), address key limitations of regression by decomposing multi-channel EEG into statistically independent components presumed to represent separate neural and artifact sources [97]. The generative model for BSS can be represented as:

x = A × s [97]

Where x represents the observed EEG signals, s contains the independent source components, and A is the mixing matrix. ICA algorithms iteratively estimate both A and s to maximize the statistical independence of components, after which artifact-related components can be identified and removed before signal reconstruction [97]. Comparative studies have demonstrated that BSS-based techniques better preserve brain activity in anterior brain regions compared to regression analysis, with significant implications for pharmaco-EEG studies where accurate anterior lead measurements are crucial [97].

Table 2: Performance Comparison of Traditional Artifact Removal Methods

Deep Learning and Emerging Approaches

Recent advances in deep learning have introduced powerful alternatives to traditional artifact removal methods, with architectures specifically designed to address the information loss trade-off. The CLEnet architecture integrates dual-scale Convolutional Neural Networks (CNN) with Long Short-Term Memory (LSTM) networks and an improved EMA-1D attention mechanism to simultaneously extract morphological and temporal features from contaminated EEG [33]. This approach has demonstrated significant performance improvements, achieving a 2.45% increase in Signal-to-Noise Ratio (SNR) and 2.65% improvement in Correlation Coefficient (CC) compared to previous methods while reducing temporal and frequency domain errors by 6.94% and 3.30% respectively [33].

The EEGOAR-Net model employs a U-Net architecture with a novel training methodology that enables montage-independent operation, eliminating the need for subject-specific calibration or EOG channels [25]. This approach effectively reduces EEG-EOG correlations to chance levels across most brain regions with minimal information loss, performing comparably to reference methods like ICA without their practical limitations [25]. Similarly, AnEEG leverages Generative Adversarial Networks (GANs) with LSTM layers to learn artifact representations while preserving temporal dependencies in neural signals [73].

Quantitative Method Comparison and Performance Metrics

Standardized Evaluation Metrics

The performance of artifact removal methods must be evaluated using multiple complementary metrics to fully capture the trade-off between artifact removal and neural information preservation. The following table summarizes key quantitative measures reported in comparative studies:

Table 3: Quantitative Performance Metrics Across Artifact Removal Methods

*Estimated values from comparative studies [97]

These quantitative measures reveal consistent patterns across methodologies. Deep learning approaches generally outperform traditional methods across multiple metrics, with CLEnet achieving the highest SNR (11.50 dB) and correlation with clean EEG (CC: 0.925) while maintaining the lowest temporal and spectral errors [33]. The performance advantages are particularly pronounced for complex artifact types and multi-channel processing scenarios.

Experimental Protocols for Method Validation

Semi-Synthetic Dataset Creation

Robust validation of artifact removal methods requires standardized datasets with known ground truth. The following protocol has emerged as a community standard:

• Clean EEG Collection: Record resting-state EEG from healthy participants under strict artifact minimization conditions (limited movement, controlled blinking)
• Artifact Recording: Simultaneously capture pure EOG signals using dedicated electrodes placed around the eyes
• Linear Mixing: Combine clean EEG and artifact signals using physiologically realistic propagation factors: RawEEG = EEG + β × EOG, where β represents channel-specific weights [33]
• Performance Quantification: Compare processed signals with original clean EEG using standardized metrics (SNR, CC, RRMSE) [33]

This approach enables controlled evaluation while maintaining physiological realism, though it may not fully capture the complex nonlinear interactions in real-world recordings.

Real-Data Validation Protocols

For real EEG data validation, researchers employ:

• Expert Visual Identification: Trained electrophysiologists identify artifact-contaminated segments
• Reference Method Comparison: Compare new methods against established techniques (ICA, regression) using multiple quantitative metrics
• Downstream Application Impact: Assess how artifact removal affects subsequent analysis (e.g., ERP components, power spectral measures, connectivity metrics) [97]

The Scientist's Toolkit: Essential Research Reagents and Materials

Table 4: Key Research Materials and Computational Tools for Artifact Removal Research

Integrated Workflow for Optimized Artifact Removal

The following workflow diagram illustrates a recommended pipeline for maximizing artifact removal while minimizing information loss, incorporating validation steps to preserve neural integrity:

Artifact Removal Decision Workflow

This integrated workflow emphasizes method selection based on channel count and application requirements, with validation metrics ensuring neural preservation. For high-channel count research systems (>40 channels), ICA and ASR provide optimal performance with appropriate component classification [35]. For low-channel scenarios or real-time applications, modern deep learning approaches like EEGOAR-Net offer calibration-free operation with minimal information loss [25].

The information loss trade-off in ocular artifact removal remains a fundamental consideration in EEG research methodology. While traditional approaches like regression and ICA established the field's foundation, emerging deep learning methods demonstrate superior performance in preserving neural signals while effectively removing contaminants [33] [73] [25]. The development of montage-independent, calibration-free approaches represents particularly significant progress for real-world applications where controlled calibration is impractical.

Future methodological development should prioritize several key areas: (1) standardized benchmarking datasets and metrics to enable direct cross-method comparisons; (2) explainable AI approaches to build trust in automated artifact removal systems; and (3) domain-adapted methods optimized for specific research contexts such pharmacological studies, where preserving specific spectral features is critical for accurate PK-PD modeling [97]. As these technical capabilities advance, researchers must maintain focus on the fundamental goal: not merely removing artifacts, but preserving the rich neural information that enables scientific discovery and clinical insight.

In electroencephalography (EEG) research, the presence of ocular artifacts—electrical signals originating from eye blinks and movements—poses a significant challenge to data integrity. These artifacts can overwhelm genuine neural signals, potentially compromising findings and hindering scientific progress. Within this context, benchmarking on public datasets emerges as a critical methodology for distinguishing genuine advancements from procedural artifacts. Benchmark datasets serve as standardized, high-quality collections of data that enable researchers to evaluate and compare the performance of algorithms, models, and systems in a fair and reproducible manner [99]. Unlike private data used for internal testing, a benchmark dataset acts as a public "measuring stick" for the entire research community, allowing objective determination of which methods offer superior accuracy, speed, or efficiency [100].

The process of benchmarking is defined as the evaluation of a dataset by comparing it with a standard [99]. In scientific machine learning, benchmarks are used by research communities to measure progress on specific problems, typically consisting of a dataset, an objective, metrics to measure progress, and reporting protocols [99]. For EEG research focused on ocular artifact correction, this translates to using standardized EEG datasets containing both clean data and artifact-contaminated segments to evaluate the performance of various correction algorithms. This standardized approach is vital for ensuring that reported improvements in artifact removal techniques represent genuine methodological advances rather than optimization to idiosyncratic private datasets.

The Ocular Artifact Challenge in EEG Analysis

Ocular artifacts manifest in EEG data as high-amplitude signals generated by the movement of the eyeball (corneo-retinal dipole) and eyelid closure. These artifacts pose a particular challenge because their amplitude can be an order of magnitude larger than cortical signals of interest, and their spectral characteristics often overlap with neural activity of clinical and research relevance [73]. Specifically, eye blinks typically produce high-amplitude, frontal-dominant signals with low-frequency components below 4 Hz, while saccadic eye movements generate characteristic spike-like potentials [101].

The impact of these artifacts on data analysis can be profound. A recent comprehensive study evaluating the impact of artifact correction on EEG/ERP decoding performance found that eyeblinks and other large artifacts can decrease the signal-to-noise ratio of EEG data, resulting in decreased statistical power for conventional univariate analyses [18]. Furthermore, the presence of uncorrected ocular artifacts can artificially inflate decoding accuracy in multivariate pattern analysis (MVPA) by providing non-neural cues for classification, potentially leading to incorrect conclusions about brain function [18]. This highlights the critical importance of effective artifact handling procedures before conducting downstream analyses.

Quantitative Impact of Artifacts on EEG Metrics

Table 1: Impact of Ocular Artifacts on Key EEG Analysis Metrics

Benchmarking Fundamentals for EEG Artifact Correction

Defining Effective Benchmark Datasets

Effective benchmarking datasets for EEG artifact correction research must possess several key characteristics to ensure valid and generalizable results. According to benchmark dataset literature, they should be standardized collections of expert-labeled data that represent the entire spectrum of challenges relevant to the problem domain [102]. For ocular artifact research, this means encompassing diverse artifact types (blinks, saccades, lateral movements), varying artifact intensities, different subject populations, and multiple recording setups [18].

The representativeness of cases encountered in clinical practice is a crucial consideration [102]. The dataset must reflect real-world scenarios, including the full spectrum of artifact severity and ensuring diversity in terms of demographics, EEG systems, and experimental paradigms. This representativeness is essential because algorithms developed on homogeneous datasets may fail when applied to data from different populations or recording environments [102].

Proper labeling constitutes another fundamental characteristic of high-quality benchmarks. For EEG artifact datasets, this typically involves expert annotation of artifact locations and types, often supplemented with ground-truth clean data for validation. The labeling process requires involvement of domain experts, and cases with poor interobserver agreement should be identified and analyzed for systematic errors [102]. When creating benchmark datasets, it is also crucial to decide on standardized annotation formats to ensure homogeneous results across research groups [102].

Limitations of Current Benchmarking Approaches

Despite their utility, current benchmarking approaches in EEG research face several significant challenges. Dataset bias can occur if the benchmark does not accurately reflect the diversity of real-world conditions [100]. For instance, an ocular artifact benchmark lacking diversity in age groups or neurological conditions may lead to methods that perform poorly for certain populations. This limitation is particularly relevant for healthcare applications, where algorithms trained on public datasets may exhibit subpar performance when applied to real-world clinical data with different demographic and pathophysiological features [102].

Another critical challenge is the community-wide overfitting that can occur when researchers repeatedly use the same public dataset for method development and evaluation [102]. As researchers strive for state-of-the-art performance on a specific benchmark, they may unconsciously optimize their methods for that particular dataset, reducing generalizability to new data. To mitigate this risk, it is common practice to evaluate new algorithms on several public and private datasets, although this only partially reduces the overall bias [102].

Table 2: Key Characteristics of Effective Benchmark Datasets for EEG Artifact Research

Experimental Protocols for EEG Benchmarking

Standardized Preprocessing Workflow

A robust protocol for semi-automatic EEG preprocessing incorporating independent component analysis (ICA) and principal component analysis (PCA) with step-by-step quality checking provides a foundation for reproducible artifact handling [101]. This protocol emphasizes three mandatory major steps: (1) basic bandpass filtering and bad channel interpolation, (2) ICA decomposition and ocular artifact removal, and (3) large-amplitude idiosyncratic artifact removal using PCA [101].

The protocol begins with proper bandpass filtering, which is critical for subsequent ICA decomposition. Studies and the official EEGLAB tutorial recommend a relatively high cutoff high-pass filter (1-2 Hz) for obtaining good ICA decomposition, which is essential for isolating major ocular artifacts [101]. However, since filtering out activity below 1 Hz may remove potentially useful neural information, the protocol provides procedures to extract ICA weights from data filtered at a higher high-pass cutoff and then apply them to data filtered at a lower high-pass cutoff [101].

Bad channel identification and interpolation using spherical spline algorithms represent a crucial preparatory step [101] [103]. This process typically involves visual inspection where noisy EEG channels are marked as bad and interpolated, with documentation of the number of channels interpolated per subject to maintain quality control [103].

Artifact Correction Methodologies

For ocular artifact correction specifically, multiple methodological approaches exist for benchmarking comparisons:

ICA-Based Correction: Independent Component Analysis remains a widely established method for EEG denoising, particularly for ocular artifacts [101] [104]. The protocol involves decomposing the EEG data into independent components, identifying those representing ocular artifacts based on their topography, timing, and spectral characteristics, and removing these components before reconstructing the signal [101]. The effectiveness of ICA depends on having a stationary data segment for decomposition, which can be achieved by selecting a dedicated segment containing ocular artifacts for the decomposition process [101].

PCA-Based Correction: Principal Component Analysis filtering algorithms represent another approach, typically implemented on specific window lengths with overlap [103]. For example, one protocol describes performing PCA correction on 800-ms windows with 400-ms overlap, using a Hamming window to control for artifacts resulting from data splicing [103]. This method targets large-amplitude transient artifacts that may not be adequately captured by ICA.

AJDC Method: The Approximate Joint Diagonalization of Fourier Cospectra (AJDC) method has emerged as a frequency-domain Blind Source Separation technique that uses cospectral analysis to isolate and attenuate blink artifacts [104]. Comparative studies with ICA have shown that AJDC effectively attenuates blink artifacts without distorting motor imagery-related beta band signatures, with preservation of neurofeedback performance [104]. This method is particularly promising for real-time applications as it can be calibrated once on initial EEG data, though periodic recalibration may benefit long recordings [104].

Deep Learning Approaches: Recent advances include methods like AnEEG, which leverages deep learning through LSTM-based GAN architectures for artifact removal [73]. These approaches train models on diverse datasets containing EEG recordings with various artifacts, with the generator creating cleaned signals and the discriminator evaluating their quality against ground-truth data [73]. Quantitative metrics including NMSE, RMSE, CC, SNR, and SAR are used to validate effectiveness [73].

Diagram 1: Comprehensive EEG Preprocessing and Benchmarking Workflow. This diagram illustrates the standardized protocol for EEG artifact correction, highlighting the iterative quality assessment and benchmarking steps essential for reproducible research.

Quantitative Evaluation Framework

Essential Performance Metrics

A comprehensive evaluation framework for EEG artifact correction methods requires multiple complementary metrics to assess different aspects of performance:

• Normalized Mean Square Error (NMSE) and Root Mean Square Error (RMSE): Quantify the difference between the cleaned signal and ground-truth clean data, with lower values indicating better agreement with the original signal [73].
• Correlation Coefficient (CC): Measures the linear relationship between cleaned and ground-truth signals, with higher values indicating stronger agreement [73].
• Signal-to-Noise Ratio (SNR) and Signal-to-Artifact Ratio (SAR): Assess the improvement in signal quality relative to noise and artifacts, with higher values indicating better performance [73].
• Preservation of Neural Signatures: For specific applications, the retention of clinically or cognitively relevant neural patterns (e.g., beta band preservation in motor imagery tasks) provides critical validation [104].

It is important to note that accuracy alone may be the least informative metric in scenarios where class imbalance exists [99]. Comprehensive evaluation should include multiple metrics such as precision, recall, and F1-score to ensure thorough assessment of algorithm performance [99].

Benchmarking Evaluation Protocols

The benchmarking process should follow standardized protocols to ensure fair comparisons:

• Dataset Partitioning: Clear separation of training, validation, and test sets, with no data leakage between partitions.
• Cross-Validation: Implementation of appropriate cross-validation strategies, considering subject-independent splits when applicable.
• Statistical Testing: Application of rigorous statistical tests to determine significant differences between methods.
• Computational Efficiency: Reporting of computational requirements and processing times for practical applicability assessment.

Recent research on benchmark reliability emphasizes that while model rankings may remain relatively stable across evaluation conditions, absolute performance scores can vary significantly [105]. This underscores the importance of evaluating robustness across multiple dataset variations and reporting both relative and absolute performance measures.

Table 3: Quantitative Performance Metrics for EEG Artifact Correction Algorithms

Table 4: Key Research Reagent Solutions for EEG Artifact Correction Research

Diagram 2: Taxonomy of EEG Artifact Correction Methods. This diagram categorizes the primary methodological approaches for addressing ocular artifacts in EEG research, highlighting both traditional and emerging techniques.

The integration of comprehensive benchmarking protocols using public datasets represents a critical pathway toward reproducible and robust EEG research. As demonstrated empirically, the systematic evaluation of artifact correction methods across diverse datasets and conditions provides essential validation that transcends the limitations of individual studies. The recent findings that artifact correction may not always improve decoding performance but remains essential to minimize confounds [18] underscores the nuanced understanding that rigorous benchmarking can provide.

Moving forward, the field must prioritize the development of more comprehensive benchmark datasets that encompass the full spectrum of artifact types, subject populations, and recording conditions encountered in real-world research and clinical practice. Furthermore, the adoption of standardized evaluation metrics and reporting guidelines will enhance comparability across studies. As new deep learning approaches continue to emerge [73], their validation against established methods through rigorous benchmarking will be essential for distinguishing genuine advancements from incremental improvements. Through these concerted efforts toward standardized benchmarking, the EEG research community can enhance the reliability and translational impact of their findings, ultimately advancing our understanding of brain function and dysfunction.

Conclusion

Ocular artifacts represent a complex, multi-source problem that requires a nuanced understanding and a methodical approach to correction. While traditional techniques like ICA remain powerful, especially when augmented with eye-tracking data, the field is rapidly advancing towards automated, calibration-free deep learning models that show great promise for wearable and real-time applications. The choice of correction strategy must be guided by the specific experimental context, including electrode density, the need for real-time processing, and the nature of the subsequent neural analysis. Crucially, recent evidence indicates that effective artifact correction is less about boosting decoding performance and more about eliminating artifactual confounds that can lead to incorrect conclusions. For biomedical and clinical research, this underscores the necessity of robust, validated preprocessing pipelines to ensure the fidelity of neural data, which is paramount for the development of reliable biomarkers and therapeutic interventions. Future directions will likely involve the wider adoption of auxiliary sensors, the standardization of benchmarking practices, and the continued refinement of deep learning models to handle the unpredictable nature of real-world EEG recordings.

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