A comparsion of artifact rejection methods for a BCI using event related potentials

Preprocessing of scalp electroencephalogram (EEG) signals to remove artifacts is essential to the reliable operation of non-invasive brain-computer interfaces (BCIs). One of the EEG-based BCIs leverages event-related potentials (ERPs) elicited by changes in specific external stimuli, which are sensitive to artifacts. To date, numerous methods have been proposed to remove artifacts from EEG. In this paper, we compare different artifact rejection methods for the operation of a BCI utilizing the ERP components such as P300 and N200, including independent component analysis (ICA), adaptive filtering, and artifact subspace reconstruction. We investigate the effect artifact removal by each method on the ERP waveform as well as BCI classification accuracy. The result demonstrates that the ERP waveforms through ICA showed a less across-trial variability in P300 amplitudes compared to other methods, as well as higher BCI classification accuracy. Our results may help the design of signal processing pipeline for EEG-based BCI systems.