EEG Motion Classification Combining Graph Convolutional Network and Self-attentiion

Lingyun Chen, Yi Niu
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Abstract

The study of EEG motor imagery adds a new therapeutic approach for patients with motor disorders, and the key to the problem study is how to improve the classification recognition of EEG motor imagery. The complex characteristics of EEG signals and the existence of multi-channel spatio-temporal properties increase the difficulty of their feature extraction and classification. There are spatial correlations between different channels and temporal correlations between different time series signals, so the selection process of signal features is complicated, resulting in low recognition rate. In this paper, we propose a spatial graph convolutional neural network based on a self-attentive mechanism. For the spatial characteristics of signals with different channels, we extract spatial features by constructing a graph structure and then by information aggregation; for its temporal characteristics, we use time slicing to calculate the importance weights of different time periods in the input signal by using the self-attentive mechanism, and then update the time segments by weighting and summing, so as to minimize the influence of other interfering signals, complete feature extraction and improve the The classification recognition rate is improved. From the experimental results, the recognition rate of this model reaches over 88% in the existing open EEG motion imagery dataset, which has good practicality and applicability.
结合图卷积网络和自注意的脑电运动分类
脑电运动图像的研究为运动障碍患者提供了一种新的治疗途径,而如何提高脑电运动图像的分类识别是问题研究的关键。脑电信号的复杂特性和多通道时空特性的存在增加了其特征提取和分类的难度。不同信道之间存在空间相关性,不同时间序列信号之间存在时间相关性,信号特征的选择过程较为复杂,导致识别率较低。本文提出了一种基于自关注机制的空间图卷积神经网络。针对不同信道信号的空间特征,先构造图结构,再进行信息聚合提取空间特征;针对其时间特征,我们采用时间切片的方法,利用自关注机制计算输入信号中不同时间段的重要权重,然后通过加权和求和对时间段进行更新,从而最大限度地减少其他干扰信号的影响,完成特征提取,提高分类识别率。实验结果表明,该模型在现有开放的脑电运动图像数据集中识别率达到88%以上,具有良好的实用性和适用性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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