Classification of single-trial motor imagery EEG by complexity regularization

Brain computer interface based on electroencephalogram is a popular way to enable communication between brain and output devices helping elderly and disabled people and in rehabilitation. In practice, the effectiveness of brain computer interface has a strong relationship with the classification accuracy of single trials. Common spatial pattern is believed to be an effective algorithm for classifying the single-trial brain signal. Since it is based on the characteristics of a broad frequency band which is manually selected and not individual variability, it is sensitive to noise and individual variability. In this paper, the common spatial pattern was extended in order to improve classification accuracies and to mitigate these influences. The channel-specific complexity weights of characteristic on montage were derived and added to improve the effects of the relevant function area and the separability between classes. The proposed method was evaluated using two public datasets, and achieved an average accuracy of 18.4% higher than conventional common spatial pattern, and the performance of the proposed method over conventional common spatial pattern was significant (p < 0.05). It indicates that the proposed method extracts subject-specific characteristics and outperforms the conventional common spatial pattern in single-trial EEG classification.