Optimizing spatial patterns with sparse filter bands for motor-imagery based brain–computer interface

Abstract

BackgroundCommon spatial pattern (CSP) has been most popularly applied to motor-imagery (MI) feature extraction for classification in brain–computer interface (BCI) application. Successful application of CSP depends on the filter band selection to a large degree. However, the most proper band is typically subject-specific and can hardly be determined manually. New methodThis study proposes a sparse filter band common spatial pattern (SFBCSP) for optimizing the spatial patterns. SFBCSP estimates CSP features on multiple signals that are filtered from raw EEG data at a set of overlapping bands. The filter bands that result in significant CSP features are then selected in a supervised way by exploiting sparse regression. A support vector machine (SVM) is implemented on the selected features for MI classification. ResultsTwo public EEG datasets (BCI Competition III dataset IVa and BCI Competition IV IIb) are used to validate the proposed SFBCSP method. Experimental results demonstrate that SFBCSP help improve the classification performance of MI. Comparison with existing methodsThe optimized spatial patterns by SFBCSP give overall better MI classification accuracy in comparison with several competing methods. ConclusionsThe proposed SFBCSP is a potential method for improving the performance of MI-based BCI.