现代电子技术2024，Vol.47Issue(18)：70-76,7.DOI:10.16652/j.issn.1004-373x.2024.18.012
基于注意力时间卷积的运动想象脑电分类方法
Method of MI-EEG classification based on attentional temporal convolution
徐嘉振 1何文雪 1李浩然1
作者信息
- 1. 青岛大学 自动化学院,山东 青岛 266071
- 折叠
摘要
Abstract
Brain-computer interface(BCI)technology based on motor imagery is helpful for the rehabilitation of patients with movement disorders,and thus it is widely used in the field of rehabilitation medicine.In allusion to the problem that the current signal-to-noise ratio of EEG signals leads to poor decoding accuracy of deep learning methods on motor imagery datasets,a method of MI-EEG(motor imagery electroencephalogram)classification based on attentional temporal convolution is proposed.The deep convolution module is used to initially extract the temporal and spatial information in the EEG signals,and three convolutional blocks with different size in the multi-scale convolution module are used to further extract the global and detailed features from the MI-EEG data.The most valuable features in the data are highlighted by means of multi-head attention module,and the advanced temporal features are extracted by means of temporal convolution network,and then the classification results are output by connected network and softmax layer.The experimental results show that,on the BCI competition IV-2b dataset,the proposed model can realize an average classification accuracy of 84.26%for the motor imagery binary classification task,and this method has significantly improved accuracy compared with existing benchmark models.关键词
脑机接口/运动想象/时间卷积网络/深度学习/多头注意力模块/多尺度卷积/信号分类Key words
brain-computer interface/motor imagery/temporal convolutional network/deep learning/multi-head attention module/multi-scale convolution/signal classification分类
信息技术与安全科学引用本文复制引用
徐嘉振,何文雪,李浩然..基于注意力时间卷积的运动想象脑电分类方法[J].现代电子技术,2024,47(18):70-76,7.
