Ocular Artifact Removal in EEG: A Review

Abstract

Electroencephalography (EEG) offers millisecond-scale temporal resolution for studying brain activity, yet its practical value is persistently compromised by ocular artifacts—unwanted electrical potentials from eye blinks and saccadic movements that can exceed genuine cortical signals by an order of magnitude. Because these artifacts share spectral content with neurologically meaningful oscillations, simple frequency-domain filtering proves inadequate. This survey covers four decades of suppression research: from regression-based subtraction and matrix factorisation (PCA, ICA) through signal-adaptive transforms (wavelet shrinkage, EMD variants) to contemporary deep-learning architectures including CNNs, GANs, and transformers. Work published from 2021 to 2026 receives particular emphasis, covering foundation-model pre-training, self-supervised artifact rejection, and edge-deployable pipelines for real-time brain–computer interface (BCI) applications. Performance is benchmarked via ∆SNR, %BAR, and RRMSE across five comparative tables and two trend figures. Ongoing challenges—ground-truth scarcity, inter-subject variability, edge-deployment constraints, and the absence of standardised benchmarks—are examined alongside prospective research directions.