Motor imagery electroencephalogram signal recognition based on mutual information and adaptive graph convolution
10.3969/j.issn.1005-202X.2025.02.014
- VernacularTitle:基于互信息与自适应图卷积的运动想象脑电信号识别
- Author:
Yelan WU
1
;
Pugang CAO
1
;
Meng XU
1
;
Yue ZHANG
1
;
Xiaoqin LIAN
1
;
Chongchong YU
1
Author Information
1. 北京工商大学计算机与人工智能学院,北京 100048
- Publication Type:Journal Article
- Keywords:
motor imagery;
electroencephalogram signal;
adaptive graph convolution;
mutual information neural estimation;
feature extraction
- From:
Chinese Journal of Medical Physics
2025;42(2):232-239
- CountryChina
- Language:Chinese
-
Abstract:
To address the challenges of extracting nonlinear features from motor imagery electroencephalogram(EEG)signals and effectively capturing functional connectivity between EEG channels,a classification and recognition method for motor imagery EEG signals is proposed based on mutual information and adaptive graph convolutional network.The proposed method extracts frequency domain information by sub-frequency banding on the original motor imagery EEG signals,uncovers the nonlinear relationships within EEG signals by an adjacency matrix constructed with mutual information neural estimation method,and finally achieve null-frequency feature extraction by capturing the dynamic correlation strength between channels with an adaptive graph convolutional network incorporating convolutional block attention module.On the BCI Competition Ⅳ 2a and BCI Competition Ⅲ 3a datasets,the proposed method has average accuracies of 83.14％and 88.19％,respectively,demonstrating that it can effectively reveal functional connectivity between EEG channels,providing a new approach for decoding motor imagery EEG signals.
