Abstract：In the research on the decoding of EEG signals, there are existing time-frequency analysis methods that have limited high-frequency signal processing capabilities, multi-channel signal information redundancy, and the ReLU activation function of the commonly used convolutional neural network classifier is greatly affected by the learning rate. It is difficult to obtain satisfactory results with the same regularization for different layers. To solve the above problems, a method based on the combination of generalized S-transform feature extraction and enhanced convolutional neural network classification is proposed. At the same time, a wrapping method combining Relief algorithm and forward selection search strategy is proposed for channel selection. The results show that the proposed method uses less signal channels and achieves better ability of feature extraction and classification. The highest classification accuracy of 98.44±1.5% is obtained in the fourth BCI dataset I, which is higher than other existing algorithms. The good classification performance of this study not only reduces the calculation consumption, also effectively improves the classification accuracy, which has a certain reference significance for EEG feature extraction and classification.
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2022, Vol. 43 
