Blind source separation (BSS) based artifact rejection systems have been extensively studied in the electroencephalogram (EEG) literature. Although there have been advances in the development of techniques capable of dissociating neural and artifactual activity, these are still not perfect. As a result, a compromise between reduction of noise and leakage of neural activity has to be found. Here, we propose a new methodology to enhance the performance of existing BSS systems: Localized component filtering (LCF). In essence, LCF identifies the artifactual time segments within each component extracted by BSS and restricts the processing of components to these segments, therefore reducing neural leakage. We show that LCF can substantially reduce the neural leakage, increasing the true acceptance rate by 22 percentage points while worsening the false acceptance rate by less than 2 percentage points in a dataset consisting of simulated EEG data (4% improvement of the correlation between original and cleaned signals). Evaluated on real EEG data, we observed a significant increase of the signal-to-noise ratio of up to 9%.
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