Construction of a deep - learning - based rehabilitation prediction model for lower-limb motor dysfunction after stroke using synchronous EEG-EMG and fMRI
A deep learning-based rehabilitation prediction model utilizing multimodal signals was successfully developed. The ShuffleNet-LSTM model exhibited excellent performance among multimodal fusion models, effectively enhancing the accuracy of predicting lower-limb motor function recovery in stroke patients.
Authors
- Yanlong Wang (ORCID: https://orcid.org/0000-0003-0120-6484)
- He Jia (ORCID: https://orcid.org/0000-0003-1042-0538)
- X.X. Wei (ORCID: https://orcid.org/0000-0002-7643-0475)
- Mingyue Fan (ORCID: https://orcid.org/0000-0001-9678-0205)
- Youyang Qu (ORCID: https://orcid.org/0000-0002-2944-4647)
- Haiyan Gou
- Hongyu Wang (ORCID: https://orcid.org/0000-0002-1038-412X)
- Jian Shi (ORCID: https://orcid.org/0000-0003-1679-4063)
- Yan Chen (ORCID: https://orcid.org/0000-0003-1950-3903)
- Yanmei Zhu (ORCID: https://orcid.org/0000-0001-8468-5227)
- Yulan Zhu
Institutions
- Harbin Medical University (CN)
- Fourth Affiliated Hospital of Harbin Medical University (CN)
- First Affiliated Hospital of Harbin Medical University (CN)
Publication Details
- Journal
- Frontiers in Neuroscience
- Published
- 2025-08-21
- DOI
- https://doi.org/10.3389/fnins.2025.1616957
- Primary Topic
- EEG and Brain-Computer Interfaces
- Type
- article
- Field-Weighted Citation Impact
- 0.00