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Virtual Reality (VR) Paradigm-Agnostic Motor Imagery Decoding Using Lightweight Network With Adaptive Attention

Rongrong Fu1,2, Yang Liu3, Zeyi Wang4

  • 1Measurement Technology and Instrumentation Key Lab of Hebei Province, Yanshan University, 066004, Qinhuangdao, China. frr1102@ysu.edu.cn.

Journal of Medical Systems
|November 2, 2025
PubMed
Summary

This study introduces an immersive virtual reality (VR) motor imagery (MI) paradigm and a novel decoding algorithm. This approach enhances neurorehabilitation by improving EEG-based intention recognition in VR environments.

Keywords:
Depth-wise separable convolutionMotor imagerySelf-attention decoderVirtual reality

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Area of Science:

  • Neuroscience
  • Rehabilitation Engineering
  • Brain-Computer Interfaces

Background:

  • Motor imagery (MI) is crucial for brain-computer interfaces (BCIs) in neurorehabilitation.
  • Existing 2D MI paradigms do not fully engage sensorimotor networks.
  • There is a need for more immersive and effective MI-based BCI approaches.

Purpose of the Study:

  • To design an immersive MI paradigm using virtual reality (VR).
  • To develop a novel decoding algorithm integrating depthwise separable convolution and multi-head self-attention.
  • To evaluate the algorithm's performance and generalizability in VR and 2D paradigms.

Main Methods:

  • Developed a VR environment with 3D palm motion stimuli for immersive MI.
  • Proposed a novel decoding algorithm combining depthwise separable convolution and multi-head self-attention.
  • Evaluated the algorithm on BCI Competition IV-2a and PhysioNet MI datasets.

Main Results:

  • The novel algorithm demonstrated superior classification accuracy compared to existing methods.
  • Achieved an average 8% increase in kappa score over EEGNet for four-class MI tasks.
  • Showed consistent performance across both VR and 2D paradigms, confirming robustness.

Conclusions:

  • The immersive VR MI paradigm enhances user engagement in neurorehabilitation.
  • The proposed decoding framework advances EEG-based intention recognition in VR.
  • This approach offers a promising avenue for improving BCI applications in motor rehabilitation.