A transfer learning framework based on motor imagery rehabilitation for stroke

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 mot...

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Bibliographic Details
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
Online Access:	Get full text
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