Recently, various deep neural networks have been applied to classify electroencephalogram (EEG) signal. EEG is a brain signal that can be acquired in a non-invasive way and has a high temporal resolution. It can be used to decode the intention of users. As the EEG signal has a high dimension of feature space, appropriate feature extraction methods are needed to improve classification performance. In this study, we obtained spatio-temporal feature representation and classified them with the combined convolutional neural networks (CNN)-gated recurrent unit (GRU) model. To this end, we obtained covariance matrices in each different temporal band and then concatenated them on the temporal axis to obtain a final spatio-temporal feature representation. In the classification model, CNN is responsible for spatial feature extraction and GRU is responsible for temporal feature extraction. Classification performance was improved by distinguishing spatial data processing and temporal data processing. The average accuracy of the proposed model was 77.70% for the BCI competition IV_2a data set. The proposed method outperformed all other methods compared as a baseline method.

中文翻译：

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基于时空特征表示的CNN-GRU网络的运动图像分类

最近，各种深度神经网络已被应用于对脑电图 (EEG) 信号进行分类。EEG 是一种可以以非侵入性方式获取并具有高时间分辨率的大脑信号。它可以用来解码用户的意图。由于脑电信号具有较高的特征空间维度，需要采用合适的特征提取方法来提高分类性能。在这项研究中，我们获得了时空特征表示，并用组合卷积神经网络 (CNN)-门控循环单元 (GRU) 模型对它们进行了分类。为此，我们在每个不同的时间带中获得协方差矩阵，然后在时间轴上将它们连接起来以获得最终的时空特征表示。在分类模型中，CNN负责空间特征提取，GRU负责时间特征提取。通过区分空间数据处理和时间数据处理来提高分类性能。对于 BCI 竞赛 IV_2a 数据集，所提出模型的平均准确率为 77.70%。与基线方法相比，所提出的方法优于所有其他方法。