Abstract

Info

Title:: Motor imagery EEG classification algorithm based on feature fusion neural network

Keywords:: Keywords: motor imagery electroencephalogram classification neural network feature fusion

Abstract:: Abstract: Motor imagery-based brain computer interface (MI-BCI) technology enables patients with movement disorders to acquire a new ability to communicate with the outside world. However, when using convolutional neural network (CNN) for MI electroencephalogram (EEG) classification, researchers often extract the features of the final convolutional layer and ignore the large amount of available information in the middle layer, resulting in poor classification performance of MI-BCI. To solve this problem, two kinds of feature fusion strategies, namely with-in model fusion-feature (WMFF) and cross model fusion-feature (CMFF), are proposed. WMFF strategy extracts the features of each CNN layer separately for feature fusion while CMFF strategy integrates CNN and long short-term memory network and extracts the features of each layer. BCI competition IV Datasets 2a is used to verify the proposed method, and the results show that the average accuracies of WMFF and CMFF for 4-category MI EEG classification reach 76.19% and 80.46%, respectively, which indicates that the proposed method can effectively improve the accuracy of MI EEG classification, and provide new ideas and methods for the application of MI-BCI.