Classification of electroencephalogram-based motor imagery (MI-EEG) tasks is crucial in brain computer interfaces (BCI). In view of the characteristics of non-stationarity, time-variability and individual diversity of EEG signals, a novel framework based on graph neural network is proposed for MI-EEG classification. First, an adaptive graph convolutional layer (AGCL) is constructed, by which the electrode channel information are integrated dynamically. We further propose an adaptive spatiotemporal graph convolutional network (ASTGCN), which fully exploits the characteristics of EEG signals in time domain and the channel correlations in spatial domain simultaneously. We execute the experiments using EEG signals recorded at motor imagery scenarios, where twenty-five healthy subjects performed MI movements of the right hand and feet to generate motor commands. Experimental results reveal that the proposed method outperforms state-of-the-art methods in terms of both classification quality and robustness. The advantages of ASTGCN include high accuracy, high efficiency, and robustness to cross-trial and cross-subject variations, making it an ideal candidate for long-term MI-EEG applications.

中文翻译：

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自适应时空图卷积网络的运动图像分类

基于脑电图的运动图像（MI-EEG）任务的分类在大脑计算机接口（BCI）中至关重要。针对脑电信号的非平稳性，时变性和个体多样性的特点，提出了一种基于图神经网络的MI-EEG分类框架。首先，构建自适应图卷积层（AGCL），通过该层动态整合电极通道信息。我们还提出了一种自适应时空图卷积网络（ASTGCN），该网络可以同时充分利用时域中的EEG信号的特征和时域中的信道相关性。我们使用在运动图像场景下记录的脑电信号执行实验，25名健康受试者进行了右手和双脚的MI运动，以产生运动命令。实验结果表明，该方法在分类质量和鲁棒性方面均优于最新方法。ASTGCN的优点包括高精度，高效率以及对交叉测试和跨主题变化的鲁棒性，使其成为MI-EEG长期应用的理想选择。