Abstract

Info

Title:: Motor imagery EEG classification algorithm using feature fusion based AEBGNet

Author(s):: DAI Liangzhou; WANG Raofen; WANG Hailing; School of Electronic and Electrical Engineering, Shanghai University of Engineering Science, Shanghai 201620, China

Keywords:: Keywords: brain-computer interface motor imagery convolutional neural network bidirectional gated recurrent unit attention mechanism

Abstract:: Abstract: To address the inability of the existing machine learning methods to simultaneously consider both the temporal and spatial domain features of electroencephalogram (EEG) signals in classifying EEG features, a feature fusion based Attention-EEGNet-BiGRU (AEBGNet) is presented for classifying motor imagery (MI) EEG signals. AEBGNet is capable of fusing the temporal domain features extracted by convolutional neural network with attention mechanism and spatial domain features extracted by a bidirectional gated recurrent unit to obtain more distinctive spatiotemporal features. The constructed AEBGNet classification model achieves an average accuracy of 80.37% on the BCI competition IV 2b dataset, and there is an improvement of 6.09% over the standard EEGNet method. The results demonstrate the effectiveness of the proposed method in enhancing the classification accuracy of MI EEG signals, providing a new idea for MI EEG signal classification.