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Complex common spatial patterns on time-frequency decomposed EEG for brain-computer interface

Motor imagery brain-computer interface (MI-BCI) has many promising applications but there are problems such as poor classification accuracy and robustness which need to be addressed. We propose a novel approach called time-frequency common spatial patterns (TFCSP) to enhance the robustness and accuracy of the electroencephalogram (EEG) signal classification. The proposed approach decomposes the EEG signal into time stages and frequency components to find the most robust and discriminative features. Common spatial patterns (CSP) are extracted from every decomposed time-frequency cell and unreliable features are removed while remaining features are weighted and regularized for the classification. Comparison on three publicly available datasets from BCI competition III and IV shows that the proposed TFCSP outperforms state-of-the-art methods. This demonstrates that adopting subject reaction time paradigm is useful to enhance the classification performance. It also shows that the complex CSP in the frequency domain significantly effective than the commonly used bandpass-filters in time domain. Finally, this work proves that weighting and regularizing CSP features are better techniques than selecting the leading CSP features because the former alleviates information loss.

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