Abstract:In order to accurately extract the optimal time period and frequency band features of individual motor imagery EEG signals and effectively improve its classification accuracy, combining convolutional neural network and integrated classification method, a new multifeature convolutional neural network (MFCNN) algorithm is proposed to classify and identify motor imagery EEG signals. Firstly, the EEG signal is preprocessed, then the original signal, energy feature, power spectrum feature and fusion feature are inputted into the convolutional neural network to obtain their respective training models. Finally, the final classification result is obtained with the weighted voting based integrated classification method. The experiment analysis of the proposed method was carried out using the 2008 BCI competition Datasets 2b dataset and the actually measured data. The results show that the proposed MFCNN method can effectively improve the recognition rate of motor imagery. The average classification accuracy and average Kappa value of all the subjects in the experiment are 786% and 057, respectively. The proposed method provides a new idea and solution for the application of motor imagery braincomputer interface.