Motor imagery EEG classification algorithm using feature fusion based AEBGNet

VernacularTitle:基于特征融合AEBGNet的运动想象脑电分类算法
Author: Liangzhou DAI ¹ ; Raofen WANG ; Hailing WANG
Author Information

1. 上海工程技术大学电子电气工程学院,上海 201620
Keywords: brain-computer interface; motor imagery; convolutional neural network; bidirectional gated recurrent unit; attention mechanism
From: Chinese Journal of Medical Physics 2024;41(8):1021-1030
CountryChina
Language:Chinese
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.