A Multi-feature Fusion Transformer Neural Network for Motor Imagery EEG Signal Classification

recent years, the classification method of motor imagery(MI) electroencephalography(EEG) signals based on deep learning(DL) has become more and more mature in the field of brain-computer interface(BIC). However, most of the studies tend to use a single feature or two associated features when dealing with motor imagery EEG signal classification, while ignoring other features, resulting in poor classification performance. Therefore, this paper proposes a neural network feature fusion algorithm called Multi-feature Fusion Transformer(M-FFT). The network is built based on convolutional neural network (CNN) and Transformer. This method uses CNN and wavelet transform to extract the time frequency and space-time features, and uses the converter to fuse the three feature domains contained in the two features to establish the information interaction between the three feature domains contained in the two features. Then, the global feature pooling is used to output the feature vector, and finally the softmax function is used to classify the feature vector. In the training process, we use cross entropy as the loss function. Finally, on the brain-computer interface competition IV data set 2a, the average classification accuracy is 85.66%, and the average kappa value is 0.833. The experimental results validate the algorithm performance.