Improved RCSP and AdaBoost-based classification for Motor-Imagery BCI

One of the popular feature extraction algorithms for motor imagery (MI)-based brain-computer interface (BCI) is common spatial pattern (CSP). However, CSP is also very susceptive to the selection of the filter bands, the time windows, and the channels. In this paper, we proposed a novel regularized CSP (RCSP) method to optimize feature extraction in MI-BCI. Then, a robust classifier based on AdaBoost algorithm was presented to perform the classification of MI tasks. Finally, the framework was verified on two public BCI datasets (dataset 1 from the BCI Competition IV and dataset IVa from BCI Competition III). The results suggest the proposed approach achieved superior performance compared with classical CSP and other competing methods. Overall, this method not only improved classification performance, but also reduced the data requirements of other subjects.