EEG Artifact Removal for Ocular Signals
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Resource Overview
This code implements artifact removal algorithms to eliminate ocular artifacts from raw EEG data, enhancing signal quality for accurate analysis.
Detailed Documentation
During electroencephalogram (EEG) signal acquisition, it is crucial to recognize that captured signals often contain substantial artifacts that may adversely affect analytical outcomes. Therefore, signal denoising becomes an essential preprocessing step. This implementation specifically targets ocular artifact removal through signal processing techniques, which may include blind source separation methods like Independent Component Analysis (ICA) or regression-based approaches. The algorithm identifies characteristic ocular artifacts patterns (such as high-amplitude frontal signals with typical blink morphology) and applies appropriate filtering or component rejection. By implementing artifact removal functions that automatically detect and subtract ocular interference, this code significantly improves EEG signal accuracy and reliability for subsequent neurological analysis.
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