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. 2021 Oct 5;11(1):19783.
doi: 10.1038/s41598-021-99114-1.

A transfer learning framework based on motor imagery rehabilitation for stroke

Fangzhou Xu  1 , Yunjing Miao  2 , Yanan Sun #  2 , Dongju Guo #  3 , Jiali Xu #  4 , Yuandong Wang #  5 , Jincheng Li #  5 , Han Li #  5 , Gege Dong #  5 , Fenqi Rong #  2 , Jiancai Leng  6 , Yang Zhang  7
Affiliations

A transfer learning framework based on motor imagery rehabilitation for stroke

Fangzhou Xu et al. Sci Rep. .

Abstract

Deep learning networks have been successfully applied to transfer functions so that the models can be adapted from the source domain to different target domains. This study uses multiple convolutional neural networks to decode the electroencephalogram (EEG) of stroke patients to design effective motor imagery (MI) brain-computer interface (BCI) system. This study has introduced 'fine-tune' to transfer model parameters and reduced training time. The performance of the proposed framework is evaluated by the abilities of the models for two-class MI recognition. The results show that the best framework is the combination of the EEGNet and 'fine-tune' transferred model. The average classification accuracy of the proposed model for 11 subjects is 66.36%, and the algorithm complexity is much lower than other models.These good performance indicate that the EEGNet model has great potential for MI stroke rehabilitation based on BCI system. It also successfully demonstrated the efficiency of transfer learning for improving the performance of EEG-based stroke rehabilitation for the BCI system.

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Conflict of interest statement

Author Jiali Xu was employed by the company Shandong Energy Group Co Ltd. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Figures

Figure 1
Figure 1
The collection process of an experiment.
Figure 2
Figure 2
The overall visualization of the EEGNet structure. The line represents the connectivity of the convolution kernel between input and output (called feature map). Where, C is the number of channels, T is the number of sampling points.
Figure 3
Figure 3
The overall average classification accuracy of all models.
Figure 4
Figure 4
(a) The highest accuracy of EEGNet for each subject. (b)Average accuracy of the two datasets (health and patients).
See this image and copyright information in PMC

References

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