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Brain-actuated gait trainer with visual and proprioceptive feedback.

Dong Liu1, Weihai Chen, Kyuhwa Lee

  • 1School of Automation Science and Electrical Engineering, Beihang University (BUAA), Beijing 100191, People's Republic of China. Defitech Chair in Brain-Machine Interface (CNBI), Center for Neuroprosthetics, Institute of Bioengineering and School of Engineering, École Polytechnique Fédérale de Lausanne (EPFL), Campus Biotech H4, 1202, Geneva, Switzerland.

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Summary

This study shows proprioceptive feedback is better than visual feedback for brain-machine interfaces (BMIs) decoding leg movements. This finding can improve neurorehabilitation strategies for motor recovery.

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Area of Science:

  • Neuroscience
  • Rehabilitation Engineering
  • Biomedical Engineering

Background:

  • Brain-machine interfaces (BMIs) offer potential for neuromodulation and neurorehabilitation.
  • Closed-loop BMIs are being explored for applications in motor control and recovery.
  • Decoding motor imagery (MI) of lower limb movements is crucial for advanced rehabilitation devices.

Purpose of the Study:

  • To investigate the impact of different feedback modalities (visual vs. proprioceptive) on the performance of an EEG-based BMI for decoding leg motor imagery.
  • To assess the feasibility of a closed-loop brain-controlled gait trainer for neurorehabilitation.
  • To analyze feature discriminability and brain pattern modulations associated with different feedback types.

Main Methods:

  • Nine able-bodied subjects participated in a crossover study using a lower-limb gait trainer (legoPress).
  • A random forest classifier was trained offline and tested online with visual and proprioceptive feedback.
  • Post-hoc analysis evaluated feedback modality impact and learning effects on simulated trial performance.

Main Results:

  • Online classification accuracy for leg MI decoding was significantly above chance level for both feedback modalities.
  • Post-hoc analysis revealed significantly better performance with proprioceptive feedback compared to visual feedback.
  • Individual feature analysis showed distinct brain patterns associated with each feedback modality, though no generalizable conclusions were drawn.

Conclusions:

  • A closed-loop, EEG-based BMI for decoding lower-limb motor imagery is feasible, demonstrating potential for neurorehabilitation.
  • Proprioceptive feedback demonstrates an advantage over visual feedback in decoding leg motor imagery.
  • Findings suggest optimizing feedback modalities can enhance robot-assisted motor training and functional recovery strategies.