A transfer learning framework based on motor imagery rehabilitation for stroke

• Published in: Scientific reports Vol. 11; no. 1; pp. 19783 - 9
• Main Authors: Xu, Fangzhou, Miao, Yunjing, Sun, Yanan, Guo, Dongju, Xu, Jiali, Wang, Yuandong, Li, Jincheng, Li, Han, Dong, Gege, Rong, Fenqi, Leng, Jiancai, Zhang, Yang
• Format: Journal Article
• Language: English
• Published: London Nature Publishing Group UK 05.10.2021; Nature Publishing Group; Nature Portfolio
• Subjects: 631/378; 639/166/985; Algorithms; Brain; Brain-Computer Interfaces; Computer applications; Data Analysis; Deep Learning; EEG; Electroencephalography; Humanities and Social Sciences; Humans; Imagery, Psychotherapy; Implants; Mental task performance; Models, Theoretical; Motor skill learning; multidisciplinary; Neural networks; Rehabilitation; Science; Science (multidisciplinary); Stroke; Stroke Rehabilitation - methods; Transfer learning; Transfer, Psychology
• ISSN: 2045-2322; 2045-2322
• DOI: 10.1038/s41598-021-99114-1

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.