Ocular Artifact Removal Using Independent Component Analysis

Abstract

Electroencephalography(EEG) recordings are prone to ocular artifacts caused by eye movement and blinks which can distort the underlying brain activity. In this study, we address the common issue of ocular artifacts in Electroencephalography (EEG) recordings, which can significantly distort the underlying brain activity and compromise the accuracy of neuroscientific analyses. Ocular artifacts are primarily caused by eye movements and blinks, creating sharp and high-amplitude deflections in the EEG signal. To mitigate these artifacts and improve the quality of EEG data, we focus on employing Independent Component Analysis (ICA) as a powerful preprocessing technique. ICA helps in identifying and seperating ocular related components from the mixed EEG signal by decomposing it. The effectiveness of the ICA method is evaluated using datasets. The result shows the efficiency of ICA in removing ocular artifacts from the EEG signals. This research contributes to the development of reliable preprocessing techniques for enhancing the quality and interpretation of EEG signals in neuroscience applications.