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Related Experiment Video

Updated: Jun 9, 2025

Author Spotlight: Enhancing Post-Stroke Upper Limb Rehabilitation with Robotic Technologies for Improved Motor Recovery and Functional Outcomes
04:49

Author Spotlight: Enhancing Post-Stroke Upper Limb Rehabilitation with Robotic Technologies for Improved Motor Recovery and Functional Outcomes

Published on: September 6, 2024

657

A 4-DOF Exosuit Using a Hybrid EEG-Based Control Approach for Upper-Limb Rehabilitation.

Zhichuan Tang1,2, Zhixuan Cui1, Hang Wang1

  • 1Industrial Design Institute, Zhejiang University of Technology Hangzhou 310023 China.

IEEE Journal of Translational Engineering in Health and Medicine
|October 28, 2024
PubMed
Summary

This study introduces a novel four-degree-of-freedom exosuit controlled by a hybrid electroencephalogram (EEG)-based system, enhancing stroke rehabilitation. The system improves active user engagement and multi-joint movement, showing promising results for motor recovery.

Keywords:
Bowden cableExosuitSSVEPmotor imageryrehabilitation

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Area of Science:

  • Biomedical Engineering
  • Neuroscience
  • Rehabilitation Science

Background:

  • Traditional stroke rehabilitation often uses rigid exoskeletons or exosuits, which may limit multi-joint involvement.
  • Enhancing active user engagement is crucial for promoting neuroplasticity and improving motor recovery post-stroke.

Purpose of the Study:

  • To develop and evaluate a four-degree-of-freedom exosuit with a hybrid electroencephalogram (EEG)-based control system for upper limb stroke rehabilitation.
  • To promote active user engagement and facilitate multi-joint rehabilitation through an innovative brain-computer interface (BCI).

Main Methods:

  • Developed a four-degree-of-freedom exosuit controlled by a hybrid EEG approach combining steady-state visual evoked potential (SSVEP) and motor imagery (MI) paradigms.
  • Utilized multivariate variational mode decomposition (MVMD) and canonical correlation analysis (CCA) for SSVEP recognition, and a convolutional neural network-long short-term memory (CNN-LSTM) model for MI recognition.
  • Translated EEG recognition results into Bowden cable control commands for multi-joint rehabilitation.

Main Results:

  • The CNN-LSTM model achieved an average classification accuracy of 90.07% ± 2.23% for MI recognition.
  • The hybrid EEG-based control system demonstrated an overall accuracy of 85.26% ± 1.95%.
  • Usability assessment yielded a high System Usability Scale (SUS) score of 81.25 ± 5.82.
  • Participants showed significant improvements, including a 10.33% average increase in range of motion (ROM) across four joints and an 11.35% increase in average electromyography (EMG) amplitude after 35 days of training.

Conclusions:

  • The developed exosuit offers accurate control, favorable usability, and efficacy in multi-joint stroke rehabilitation.
  • The hybrid EEG-based control approach effectively enhances active user engagement, aligning with neuroplasticity principles.
  • This study provides novel insights and methods for BCI-based rehabilitation strategies and hardware development, potentially improving clinical outcomes.