A brain-computer interface (BCI) based on electroencephalography (EEG) can provide independent information exchange and control channels for the brain and the outside world. However, EEG signals come from multiple electrodes, the data of which can generate multiple features. How to select electrodes and features to improve classification performance has become an urgent problem to be solved. This paper proposes a deep convolutional neural network (CNN) structure with separated temporal and spatial filters, which selects the raw EEG signals of the electrode pairs over the motor cortex region as hybrid samples without any preprocessing or artificial feature extraction operations. In the proposed structure, a 5-layer CNN has been applied to learn EEG features, a 4-layer max pooling has been used to reduce dimensionality, and a fully-connected (FC) layer has been utilized for classification. Dropout and batch normalization are also employed to reduce the risk of overfitting. In the experiment, the 4 s EEG data of 10, 20, 60, and 100 subjects from the Physionet database are used as the data source, and the motor imaginations (MI) tasks are divided into four types: left fist, right fist, both fists, and both feet. The results indicate that the global averaged accuracy on group-level classification can reach 97.28%, the area under the receiver operating characteristic (ROC) curve stands out at 0.997, and the electrode pair with the highest accuracy on 10 subjects dataset is FC3-FC4, with 98.61%. The research results also show that this CNN classification method with minimal (2) electrode can obtain high accuracy, which is an advantage over other methods on the same database. This proposed approach provides a new idea for simplifying the design of BCI systems, and accelerates the process of clinical application.

中文翻译：

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基于电极对信号的 MI-EEG 简化 CNN 分类方法


基于脑电图（EEG）的脑机接口（BCI）可以为大脑与外界提供独立的信息交换和控制通道。然而，脑电图信号来自多个电极，其数据可以产生多种特征。如何选择电极和特征来提高分类性能已成为迫切需要解决的问题。本文提出了一种具有分离的时间和空间滤波器的深度卷积神经网络（CNN）结构，该结构选择运动皮层区域上电极对的原始脑电信号作为混合样本，而无需任何预处理或人工特征提取操作。在所提出的结构中，应用 5 层 CNN 来学习 EEG 特征，使用 4 层最大池化来降低维度，并利用全连接（FC）层进行分类。还采用 dropout 和批量归一化来降低过度拟合的风险。实验中，以Physionet数据库中10、20、60、100名被试的4 s脑电数据为数据源，将运动想象（MI）任务分为四种类型：左拳、右拳、双拳，双脚。结果表明，群体级分类的全局平均准确率可达97.28%，受试者工作特征（ROC）曲线下面积达到0.997，10个受试者数据集上准确率最高的电极对是FC3-FC4 ，达98.61%。研究结果还表明，这种具有最小（2）电极的CNN分类方法可以获得较高的准确率，这比同一数据库上的其他方法具有优势。 该方法为简化BCI系统的设计提供了新的思路，并加速了临床应用的进程。