基于双分支卷积神经网络的运动想象分类

EEG signals are the overall reflection of brain nerve cell electrophysiological activities on the cerebral cortex or scalp surface. EEG signals contain a large amount of physiological and disease information, and are often used in brain-computer interface research and rehabilitation treatment of stroke patients. The key to solving these problems is how to improve the accuracy of EEG signal recognition. Hence, this paper proposes a end-to-end double branch convolutional neural network for feature extraction and classification of motor imaging EEG signals. First, the double branch convolutional neural network is used to automatically extract multiple features of the original EEG signals; Then, the residual module is used for feature fusion; finally, the fully connected network layer is used to classify the EEG signals. The experiment has achieved good results in the BCI competition III 4a dataset, the proposed model has higher application value for it classifies motor imagery EEG signals efficiently without manual feature extraction when spatial information is insufficient.