Improved Brain–Computer Interface Signal Recognition Algorithm Based on Few-Channel Motor Imagery

Frontiers in Human Neuroscience

Fan Wang¹  Yunfa Fu¹  Jianhua Zhou¹  Huadong Liu¹  Lei Su¹  Lei Zhao³  Anmin Gong⁴ 

[1] Brain Cognition and Brain-Computer Intelligence Integration Group, Kunming University of Science and Technology, Kunming, China;Faculty of Information Engineering and Automation, Kunming University of Science and Technology, Kunming, China;Faculty of Science, Kunming University of Science and Technology, Kunming, China;School of Information Engineering, Chinese People’s Armed Police Force Engineering University, Xi’an, China;

关键词: MI-BCI with fewer channels;    Dempster–Shafer evidence theory;    time-frequency decomposition (TFD);    phase space reconstruction (PSR);    common spatial pattern (CSP);   

DOI  :  10.3389/fnhum.2022.880304

来源: DOAJ

【 摘 要 】

Common spatial pattern (CSP) is an effective algorithm for extracting electroencephalogram (EEG) features of motor imagery (MI); however, CSP mainly aims at multichannel EEG signals, and its effect in extracting EEG features with fewer channels is poor—even worse than before using CSP. To solve the above problem, a new combined feature extraction method has been proposed in this study. For EEG signals from fewer channels (three channels), wavelet packet transform, fast ensemble empirical mode decomposition, and local mean decomposition were used to decompose the band-pass filtered EEG into multiple time–frequency components, and the corresponding components were selected according to the frequency characteristics of MI or the correlation coefficient between its time–frequency components and the original EEG signal. Furthermore, phase space reconstruction (PSR) was performed on the selected components after the three time-frequency decompositions, the maximum Lyapunov index was calculated, and the features were reconstructed; then, CSP projection mapping was used for the reconstructed features. The support vector machine probability output model was trained by the obtained three mappings. Probability outputs by three different support vector machines were then obtained. Finally, the classification of test samples was determined by the fusion of the Dempster–Shafer evidence theory at the decision level. The results showed that the accuracy of the proposed method was 95.71% on data set III of BCI competition II (left- and right-hand MI), which was 2.88% higher than the existing methods. On data set IIb of BCI competition IV, the average accuracy was 86.60%, which was 2.3% higher than the existing methods. This study verified the effectiveness of the proposed method and provided an approach for the research and development of the MI-BCI system based on fewer channels.

【 授权许可】

Unknown