Abstract
Deep learning models have received a lot of attention from researchers in the field of brain-computer interface (BCI) due to their successful applications in the field of natural language processing as well as computer vision. The research shows that the convolutional neural network can be effectively applied to the classification and recognition of motion imagery (MI) signals, and has achieved good results. How to extract the spatial domain and time-frequency domain features of EEG signals and how to construct a sample matrix suitable for network training have been the key research problems in the field of EEG classification. In this paper, we start from the existing model and conduct experiments on the BCI public data set BCI Competition Set IV2a to investigate the feature bands of signals in different frequency band ranges, to construct the optimal feature matrix to improve the classification accuracy of the model. The experiments compare two traditional algorithms and an algorithm applying an artificial neural network.
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