A common spatial pattern based corticomuscular coherence feature extraction method for movement intention decoding

The combined analysis of electroencephalogram (EEG) and electromyogram (EMG) is often used for the recognition of motor intentions in the research of brain-computer interfaces. Corticomuscular coherence (CMC) is one of the most promising features in the combined analysis of EEG and EMG. However, traditional CMC analysis usually only focuses on the correlation between the information of a single EEG channel and sEMG, ignoring the spatial information of the neuromuscular connectivity. In this study, we proposed a novel feature extraction method based on common spatial pattern(CSP) for extracting the coherence between multi-channels EEG and sEMG, and applied this method to decode a five-class motor execution task of upper limbs. As a result, the average accuracy of the proposed CSP-CMC method reached 98.7%, which was significant higher than that of other combinations of feature extraction methods. The results demonstrate that the CSP-CMC based method proposed in this study exhibits excellent performance in the recognition of motion execution tasks and is expected to promote the development of applications in neural rehabilitation.