基于集成式张量域自适应的运动想象脑电分类

doi:10.3969/j.issn.0258-8021.2024.04.002

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Abstract In clinical applications, EEG signals have been facing problems including high acquisition cost and large differences between users, which restrict the development of motor imaging based on EEG signals. Aiming at the task of cross-subject MI-EEG recognition, a transfer learning method based on ensemble tensor domain adaptation was proposed in this paper. Firstly, the improved Euclidean alignment method was used to co-align the multidimensional EEG data to eliminate the edge distribution shift of the original data. Secondly, an improved joint distribution adaptation method based on tensor subspace was proposed, which obtained different classes of mapping subspaces and performed label prediction of target domain samples. In this paper, experiments were carried out on BCI datasets of 200 samples for 7 people and 144 samples for 9 people, which proved that the proposed method had good performance in cross-domain classification recognition with average accuracy 82.18 % and 76.45 %. The effect of each part of the method was also visually verified, which showed the effectiveness of the ensemble method on cross-domain problems.

Key words： motor image EEG domain adaptation data alignment tensor subspace

Received: 24 February 2023

PACS:

R318

Corresponding Authors: ^*E-mail: gyy@hdu.edu.cn

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	Articles by authors
	Gao Yunyuan
	Xue Yunfeng
	Zhang Congrui
	Gao Jan

Cite this article:

Gao Yunyuan,Xue Yunfeng,Zhang Congrui, et al. MI-EEG Classification Based on Ensemble Tensor Domain Adaptation[J]. Chinese Journal of Biomedical Engineering, 2024, 43(4): 399-407.

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http://cjbme.csbme.org/EN/10.3969/j.issn.0258-8021.2024.04.002 OR http://cjbme.csbme.org/EN/Y2024/V43/I4/399

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