计算机工程与应用2024，Vol.60Issue(11)：115-128,14.DOI:10.3778/j.issn.1002-8331.2301-0173
融入时空自适应图卷积的运动想象信号解析
Motion Imagery Signal Analysis Incorporating Spatio-Temporal Adaptive Graph Convolution
摘要
Abstract
Brain-computer interface(BCI)technology based on motor imagery(MI)EEG signals has been widely con-cerned and studied in the medical application of motor function rehabilitation for stroke patients.However,the MI signal has the characteristics of low signal-to-noise ratio and large volume variability,which leads to excessive noise in the EEG signal and affects the classification performance.Therefore,how to fully extract MI signal features to obtain higher single-subject classification accuracy,and how to train a general model with excellent cross-subject performance are urgent prob-lems to be solved when MI-BCI system is used in practical applications.In response to this problem,this paper proposes a spatiotemporal adaptive graph convolutional network model for different subjects,which extracts MI feature signals from two dimensions of time and spatio for classification.The model includes four modules:spatial adaptive graph convolution module,temporal adaptive graph convolution module,feature fusion module and feature classification module.The spa-tial adaptive graph convolution module dynamically constructs the spatial graph representation through feature similarity between channels,and gets rid of the limitation of artificially constructs graph representation.The time-adaptive graph convolution module divides the time series of EEG signals into multiple time segments and calculates the similarity between time segments,so as to adaptively construct the time map representation of EEG signals and eliminate the influence of noise.Finally,feature fusion and classification are performed.The results show that the proposes method achieves an aver-age classification accuracy of 90.45%and 91.64%is achieved by using 10-fold cross-validation method on BCIIV2a data-set and 91.64%on HGD dataset.Compared with the current state-of-the-art methods,this method achieves a higher accu-racy rate,proving the effectiveness of our model.By using transfer learning to experiment on different individuals,the average accuracy is increased by 1.66 percentage points,which proves the robustness of the model.关键词
脑机接口/运动想象/深度学习/图卷积Key words
brain-computer interface/motor imagery/deep learning/graph convolution network分类
信息技术与安全科学引用本文复制引用
刘京,康晓慧,董泽浩,李璇,赵薇,王余..融入时空自适应图卷积的运动想象信号解析[J].计算机工程与应用,2024,60(11):115-128,14.基金项目
河北省自然科学基金(F2020205006) （F2020205006）
河北省高等学校科学技术研究项目"青年拔尖人才"(BJ2020059) （BJ2020059）
中央引导地方科技发展资金项目(226Z0303G). （226Z0303G）
