Independent Component Analysis and Multiresolution Asymmetry Ratio for Brain–Computer Interface

This study proposes a brain-computer interface (BCI) system for the recognition of single-trial electroencephalogram (EEG) data. With the combination of independent component analysis (ICA) and multiresolution asymmetry ratio, a support vector machine (SVM) is used to classify left and right finger lifting or motor imagery. First, ICA and similarity measures are proposed to eliminate the electrooculography (EOG) artifacts automatically. The features are then extracted from the wavelet data by means of an asymmetry ratio. Finally, the SVM classifier is used to discriminate between the features. Compared to the EEG data without EOG artifact removal, band power, and adoptive autoregressive (AAR) parameter features, the proposed system achieves promising results in BCI applications.