Motor imagery EEG classification algorithm based on CNN-LSTM feature fusion network

Motor imagery brain-computer interface (MI-BCI) provides a novel way for human-computer interaction. Traditional neural networks often use serial structure to extract spatial features when dealing with motor imagery EEG signal classification, ignoring temporal information and a large amount of available information in the middle layer, resulting in poor classification performance of MI-BCI. A neural network feature fusion algorithm is proposed by combining the convolutional neural network (CNN) and the long-short-term memory network (LSTM). Specifically, the CNN and LSTM are connected in parallel. The CNN extracts spatial features, the LSTM extracts temporal features and the flatten layer added after the convolutional layer extracts the middle layer features. Then all the features are merged in the fully connected layer to improve the accuracy of classification. The average accuracy and Kappa value of all subjects were 87.68% and 0.8245, respectively. The result shows that the feature fusion neural network proposed in this paper can effectively improve the accuracy of motor imagery EEG, and provides new ideas for the study of feature extraction and classification of motor imagery brain-computer interfaces.