A method for recognizing motor imagery EEG signals based on high-quality lead selection

In the application of motor imagery brain-computer interface system, high-density leads bring redundant noise, which leads to time-consuming system operation and poor performance. A channel selection strategy based on brain function network is proposed. This method introduces Synchronization likelihood was used as a connection index to construct a motor imagery brain functional network, and the centrality analysis of the constructed network was used to select the combination of strong motor-related leads. Experiments were carried out on the EEG datasets dataset IVa of the 3rd International Brain-Computer Interface Competition and dataset I of the 4th International Brain-Computer Interface Competition, and 27 high-quality channels were selected from the 118 channels of dataset IVa, as well as 16 high-quality channels were selected from the 59 channels of dataset I. Finally, the CSP algorithm and support vector machine are used to extract features and classify. The experimental results show that the proposed channel selection strategy can greatly reduce the number of channels while obtaining higher recognition accuracy, which verifies the practicability and effectiveness of the proposed method.