针对机器学习方法在对脑电特征进行分类时无法同时兼顾脑电信号的时-空域特征的问题，利用添加注意力机制的卷积神经网络提取空间特征和双向门控循环单元提取时间特征，提出一种基于特征融合的运动想象(Motor Imagery，MI)脑电分类算法(Attention-EEGNet-BiGRU，AEBGNet)，AEBGNet可将时、空域两类特征相融合，得到更具表征性的时-空域特征，最终构建的AEBGNet分类模型在BCI competition IV 2b数据集上取得80。37%的平均正确率，比标准的EEGNet方法提高6。09%。结果表明，本文方法可以有效提高MI脑电信号的分类正确率，为MI脑电信号的分类提供新的思路。
Motor imagery EEG classification algorithm using feature fusion based AEBGNet
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.
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