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Multimodal brain-controlled system for rehabilitation training: Combining asynchronous online brain-computer

Lei Liu1, Jian Li1, Rui Ouyang1

  • 1School of Computer Science and Technology, Anhui University, Hefei 230601, China; Anhui Province Key Laboratory of Multimodal Cognitive Computation, Anhui University, Hefei 230601, China.

Journal of Neuroscience Methods
|April 11, 2024
PubMed
Summary
This summary is machine-generated.

This study introduces a brain-controlled exoskeleton system for active rehabilitation, enhancing limb function through a multimodal brain-computer interface (BCI). The system achieved high accuracy, promoting neural remodeling for improved patient outcomes.

Keywords:
Brain–computer interfaceMotor imageryMovement impairmentRehabilitationSteady-state visual evoked potential

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Area of Science:

  • Neuroscience
  • Rehabilitation Engineering
  • Human-Computer Interaction

Background:

  • Traditional rehabilitation is labor-intensive and costly.
  • Existing human-computer interaction (HCI) methods like robot-assisted therapy and functional electrical stimulation (FES) show limited nerve remodeling due to low brain engagement.
  • There is a need for cost-effective and efficient rehabilitation solutions that promote neural recovery.

Purpose of the Study:

  • To propose a multimodal brain-controlled active rehabilitation system using a brain-computer interface (BCI) and exoskeleton.
  • To enhance limb function recovery and promote damaged nerve remodeling.
  • To improve patient engagement and communication, particularly for those with aphasia.

Main Methods:

  • A novel multimodal brain-controlled active rehabilitation system integrating BCI and exoskeleton technology.
  • Utilizing a joint control mode of steady-state visual evoked potential (SSVEP) and motor imagery (MI) for self-paced control.
  • Incorporating a Transformer model as the motor imagery (MI) decoder for improved electroencephalogram (EEG) signal processing.
  • Adding an SSVEP-based requirement selection function for enhanced communication with aphasia patients.

Main Results:

  • Achieved high accuracy rates of 91.25% and 92.50% in online experiments involving left hand, right hand, foot, and idle states.
  • Demonstrated the system's effectiveness in a multi-task online setting.
  • Validated the performance through both offline and online experimental evaluations.

Conclusions:

  • The developed system offers a high-performance, low-latency brain-controlled rehabilitation solution.
  • It provides an independent and autonomous brain control mode to maximize brain involvement.
  • The system shows potential for improving the efficacy of neural remodeling in rehabilitation.