Your new experience awaits. Try the new design now and help us make it even better

ORIGINAL RESEARCH article

Front. Neurosci.

Sec. Brain Imaging Methods

Volume 19 - 2025 | doi: 10.3389/fnins.2025.1616957

Construction of a Deep -Learning -Based Rehabilitation Prediction Model for Lower-Limb Motor Dysfunction after Stroke Using Synchronous EEG-EMG and fMRI

Provisionally accepted
Jiaqi  ShiJiaqi Shi1洪玉  王洪玉 王1Haiyan  GouHaiyan Gou1Yan  ChenYan Chen1Jia  HeJia He1Youyang  QuYouyang Qu1Xinya  WeiXinya Wei2Mingyue  FanMingyue Fan3Yanlong  WangYanlong Wang1*Yanmei  ZhuYanmei Zhu1*Yulan  ZhuYulan Zhu1*
  • 1The Second Affiliated Hospital of Harbin Medical University, Harbin, China
  • 2The Fourth Hospital of Harbin Medical University, Harbin, Heilongjiang Province, China
  • 3First Affiliated Hospital of Harbin Medical University, Harbin, Heilongjiang Province, China

The final, formatted version of the article will be published soon.

ABSTRACT Objective: Construct a predictive model for rehabilitation outcomes in ischemic stroke patients three months post-stroke using resting state functional magnetic resonance imaging(fMRI) images, as well as synchronized electroencephalography (EEG) and electromyography (EMG) time series data. Methods: A total of 102 hemiplegic patients with ischemic stroke were recruited. Resting - state functional magnetic resonance imaging (fMRI) scans were carried out on all patients and 86 of them underwent simultaneous electroencephalogram (EEG) and electromyogram (EMG) examinations.After data preprocessing, we established prediction models based on time-series data and fMRI images separately.The predictions of the time - series model and the fMRI model were integrated using ensemble learning methods to create a multimodal fusion prediction model. The accuracy, recall, precision, F1 - score, and the area under the ROC curve(AUC) were calculated to evaluate the performance of the model. Results: Compared to unimodal prediction models, multimodal fusion models demonstrated superior predictive performance. The ShuffleNet-LSTM model outperformed other multimodal fusion approaches. The area under the ROC curve was 0.8665, accuracy was 0.8031, F1-score was 0.7829, recall was 0.774, and precision was 0.833. Conclusions: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.

Keywords: Rehabilitation prediction model, ischemic stroke, deep learning, Model visualization, Motor dysfunction

Received: 24 Apr 2025; Accepted: 04 Aug 2025.

Copyright: © 2025 Shi, 王, Gou, Chen, He, Qu, Wei, Fan, Wang, Zhu and Zhu. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

* Correspondence:
Yanlong Wang, The Second Affiliated Hospital of Harbin Medical University, Harbin, China
Yanmei Zhu, The Second Affiliated Hospital of Harbin Medical University, Harbin, China
Yulan Zhu, The Second Affiliated Hospital of Harbin Medical University, Harbin, China

Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.