Due to the limitations in the accuracy and robustness of current electroencephalogram (EEG) classification algorithms, applying motor imagery (MI) for practical Brain-Computer Interface (BCI) applications remains challenging. This paper proposed a model that combined a three-dimensional convolutional neural network (CNN) with a long short-term memory (LSTM) network with attention to classify MI-EEG signals. This model combined MI-EEG signals from different channels into three-dimensional features and extracted spatial features through convolution operations with multiple three-dimensional convolutional kernels of different scales. At the same time, to ensure the integrity of the extracted MI-EEG signal temporal features, the LSTM network was directly trained on the preprocessed raw signal. Finally, the features obtained from these two networks were combined and used for classification. Experimental results showed that this model achieved a classification accuracy of 92.7% and an F1-score of 0.91 on the public dataset BCI Competition IV dataset 2a, which were both higher than the state-of-the-art models in the field of MI tasks. Additionally, 12 participants were invited to complete a four-class MI task in our lab, and experiments on the collected dataset showed that the 3D-CLMI model also maintained the highest classification accuracy and F1-score. The model greatly improved the classification accuracy of users' motor imagery intentions, giving brain-computer interfaces better application prospects in emerging fields such as autonomous vehicles and medical rehabilitation.

翻译：由于当前脑电信号（EEG）分类算法在准确性和鲁棒性方面的局限性，将运动想象（MI）应用于实际的脑机接口（BCI）系统仍面临挑战。本文提出了一种结合三维卷积神经网络（CNN）与基于注意力机制的长短期记忆网络（LSTM）的模型，用于对MI-EEG信号进行分类。该模型将不同通道的MI-EEG信号整合为三维特征，并通过多个不同尺度的三维卷积核进行卷积操作以提取空间特征。同时，为确保所提取的MI-EEG信号时间特征的完整性，LSTM网络直接对预处理后的原始信号进行训练。最后，将这两个网络获得的特征进行融合并用于分类。实验结果表明，该模型在公开数据集BCI Competition IV dataset 2a上实现了92.7%的分类准确率和0.91的F1分数，均高于MI任务领域现有最优模型。此外，我们邀请12名参与者在实验室完成四分类MI任务，在采集的数据集上进行实验的结果显示，3D-CLMI模型仍保持最高的分类准确率和F1分数。该模型大幅提升了用户运动想象意图的分类精度，使得脑机接口在自动驾驶和医疗康复等新兴领域具有更好的应用前景。