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

For FUCKING STUPIDITY'S SAKE!  Predictions DO NOTHING FOR RECOVERY! Are you that blitheringly stupid? Real question, needing an answer!

Send me personal hate mail on this: oc1dean@gmail.com. I'll print your complete statement with your name and my response in my blog. Or are you afraid to engage with my stroke-addled mind? No excuses are allowed! You're medically trained; it should be simple to precisely state EXACTLY HOW predictions get you to 100% recovery with NO EXCUSES! Your definition of competence in stroke is obviously much lower than stroke survivors' definition of your competence! Swearing at me is allowed, I'll return the favor. Don't even attempt to use the excuse that brain research is hard.

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

Jiaqi Shi¹洪玉 王¹Haiyan Gou¹Yan Chen¹Jia He¹Youyang Qu¹Xinya Wei²Mingyue Fan³Yanlong Wang^1*Yanmei Zhu^1*Yulan Zhu^1*

• 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

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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.