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Towards a non-invasive brain-machine interface system to restore gait function in humans.

Alessandro Presacco1, Larry Forrester, Jose L Contreras-Vidal

  • 1Department of Kinesiology, University of Maryland, College Park, MD 20742, USA. apresacc@umd.edu

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
|January 19, 2012
PubMed
Summary

This study demonstrates that electroencephalography (EEG) can decode human walking, paving the way for non-invasive brain-machine interfaces to restore gait function.

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Area of Science:

  • Neuroscience
  • Biomedical Engineering
  • Rehabilitation Technology

Background:

  • Brain-machine interfaces (BMIs) were previously limited to upper limb prosthetics.
  • Decoding bipedal locomotion was thought to involve minimal supraspinal control and was hindered by EEG signal artifacts during walking.
  • Prior research showed cortical neuron recordings could decode primate locomotion.

Purpose of the Study:

  • To investigate the feasibility of decoding human bipedal locomotion using non-invasive electroencephalography (EEG) signals.
  • To establish a foundation for developing neural interfaces for gait restoration.

Main Methods:

  • Six healthy adults walked on a treadmill while their EEG and lower limb joint kinematics (hip, knee, ankle) were recorded.
  • Participants received visual feedback to guide their steps, avoiding a marked strip.
  • EEG data was analyzed to predict lower limb joint movements during walking.

Main Results:

  • The study successfully decoded human walking kinematics from EEG signals.
  • Average correlation between predicted and recorded kinematics was 0.7 for the right leg and 0.66 for the left leg.
  • Average signal-to-noise ratios for predicted parameters were 3.36 dB (right leg) and 2.79 dB (left leg).

Conclusions:

  • Non-invasive EEG signals can be used to decode human bipedal locomotion.
  • These findings support the development of non-invasive neural interfaces for volitional control of devices to restore gait function.