黎曼流形切平面空间中运动想象脑电信号的迁移学习

doi:10.3969/j.issn.0258-8021.2023.06.003

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Abstract Aiming at the few-shot problem of motor imagery EEG signals, most of the existing analysis methods are based on Riemannian manifolds design classifiers after alignment in the manifold, or classify in Euclidean space after tangent space projection. It is complicated to design classifier operation in manifold, and direct classification in tangent space will lead to errors due to the inconsistency of tangent space distribution between the source and target domain. Therefore, a transfer learning algorithm based on Riemannian manifold was proposed in this study to classify motor imagery EEG signals. By calculating the covariance matrices of the source and target domain and obtaining their Riemannian mean as the tangent points, the covariance matrices were respectively mapped into the tangent spaces, and then the two tangent spaces were aligned by using a set of common feature bases to complete transfer learning. Three motor imagery datasets with 7, 9 and 9 subjects were used for validation, and there were 300, 144 and 120 samples in the three datasets, respectively. The performance of the algorithm was evaluated using classification accuracy, data distribution map and statistical methods. The average classification accuracy of the proposed algorithm on the three data sets is 81.45%, 77.14% and 66.94% respectively, which increased by 25.05%, 17.69% and 10.98% higher compared with that of the model without transfer learning. The Mann-Whitney U test verified that the difference between the classification results under the two models was statistically significant. Compared with other four comparison algorithms, the performance of the proposed algorithm was significantly different, which showed the superiority of the proposed algorithm. The proposed algorithm can effectively reduce the distribution difference of data between different domains, improve the classification accuracy of cross-subject data, and achieve the expansion of few-shot data to a certain extent.

Key words： Riemannian manifold transfer learning motor imagery electroencephalogram (EEG) test

Received: 19 August 2021

PACS:

R318

Corresponding Authors: ^*E-mail: hehong@usst.edu.cn

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	Xu Hui
	He Hong
	Zhang Huiming
	Zhang Li

Cite this article:

Xu Hui,He Hong,Zhang Huiming, et al. Transfer Learning for Motor Imagery EEG Signals in Riemannian Manifold Tangent Space[J]. Chinese Journal of Biomedical Engineering, 2023, 42(6): 659-667.

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http://cjbme.csbme.org/EN/10.3969/j.issn.0258-8021.2023.06.003 OR http://cjbme.csbme.org/EN/Y2023/V42/I6/659

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