Lun Xiangmin, Yu Zhenglin, Chen Tao, Wang Fang, Hou Yimin
College of Mechanical and Electric Engineering, Changchun University of Science and Technology, Changchun, China.
School of Automation Engineering, Northeast Electric Power University, Jilin, China.
Front Hum Neurosci. 2020 Sep 15;14:338. doi: 10.3389/fnhum.2020.00338. eCollection 2020.
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.
基于脑电图（EEG）的脑机接口（BCI）能够为大脑与外界提供独立的信息交换和控制通道。然而，EEG信号来自多个电极，其数据会产生多种特征。如何选择电极和特征以提高分类性能已成为亟待解决的问题。本文提出一种具有分离时空滤波器的深度卷积神经网络（CNN）结构，该结构将运动皮层区域电极对的原始EEG信号作为混合样本，无需任何预处理或人工特征提取操作。在所提出的结构中，应用了一个5层CNN来学习EEG特征，使用了一个4层最大池化来降维，并利用一个全连接（FC）层进行分类。还采用了随机失活（Dropout）和批量归一化来降低过拟合风险。在实验中，将来自Physionet数据库的10、20、60和100名受试者的4秒EEG数据用作数据源，并将运动想象（MI）任务分为四种类型：左拳、右拳、双拳和双脚。结果表明，组级分类的全局平均准确率可达97.28%，受试者工作特征（ROC）曲线下面积高达0.997，在10名受试者数据集上准确率最高的电极对是FC3 - FC4，为98.61%。研究结果还表明，这种使用最少（2个）电极的CNN分类方法能够获得高精度，这比同一数据库上的其他方法更具优势。所提出的方法为简化BCI系统设计提供了新思路，并加速了临床应用进程。
