LAUSR

BACKGROUND Removing artifacts is a prerequisite step for the analysis of electroencephalographic (EEG) signals. Artifacts appear in both time and time-frequency as well as spatial (multi-channel) domains. NEW METHODS Here, we introduce two novel methods for removing EEG artifacts. In the first method, the common components among EEG channels are extracted and eliminated as artifacts, called common component rejection (CCR). In the second method, wavelet decomposition is employed to decompose the EEG signals, then the CCR method is applied to remove artifacts in the time-frequency domain, referred to as automatic wavelet CCR (AWCCR). The proposed methods are evaluated using semi-simulated data as well as application in real EEG data for motor imaginary classification. RESULTS For semi-simulated data, the AWCCR showed higher performance in removing artifacts than CCR. Also, applying each of the proposed methods to the real EEG data to remove artifacts before motor imaginary classification increased the classification accuracy by about 10% compared to not removing artifacts. COMPARISON WITH EXISTING METHODS The proposed methods are compared with independent component analysis (ICA) and automatic wavelet ICA. AWCCR outperformed all methods in removing artifacts from semi-simulated data. The results also showed that both AWCCR and CCR methods outperformed the existing methods in removing artifacts from the real EEG data to improve the accuracy of motor imaginary classification. CONCLUSIONS The findings show that in ordinary or motor imaginary EEG when signatures of artifacts are shared among EEG channels, AWCCR and CCR can identify and remove the artifacts.