Affiliations: [a] Chongqing Vocational Institute of Safety Technology, Chongqing 404020, China | [b] Chongqing University Three Gorges Hospital, Chongqing 404000, China
Correspondence:
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Corresponding author: Chunjiang Wan, Chongqing University Three Gorges Hospital, Chongqing 404000, China. E-mail: [email protected].
Abstract: Brain-computer interface (BCI) is an emerging paradigm to achieve communication between external devices and the human brain. Due to the low signal-to-noise ratio of the original electroencephalograph (EEG) signals, it is different to achieve feature extraction and feature selection, and further high classification accuracy cannot be obtained. To address the above problems, this paper proposes a pattern recognition method that takes into account sample entropy combined with a batch-normalized convolutional neural network. In addition, the sample entropy is used to extract features from the EEG signal data processed by wavelet transform and independent component analysis, and then the extracted data are fed into the convolutional neural network structure to recognize the EEG signal. Based on the comparison of experimental results, it is found that the method proposed in this paper has a high recognition rate.
