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Decoding neural activity preceding balance loss during standing with a lower-limb exoskeleton using an interpretable

Akshay Sujatha Ravindran1,2, Christopher A Malaya3,2, Isaac John3,2

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|May 4, 2022
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Summary

Electroencephalography (EEG) can predict impending falls within milliseconds by detecting brain activity changes. This neural information may enable brain-machine interfaces for fall prevention in wearable robotic systems.

Keywords:
CNNEEGdeep learningexoskeletonfall preventioninterpretabilityperturbation evoked potential

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Area of Science:

  • Neuroscience
  • Robotics
  • Biomechanics

Background:

  • Falls are a major cause of mortality in older adults.
  • Wearable robotic systems aim to restore lower-limb function but require effective fall prevention.
  • Early detection of balance loss is crucial for fall prevention in robotic systems.

Purpose of the Study:

  • To investigate electroencephalography (EEG) as a predictor of impending falls.
  • To understand the brain's response to perturbations for fall avoidance.
  • To explore the utility of EEG in conjunction with robotic exoskeletons for fall prevention.

Main Methods:

  • Acquired EEG, electromyography (EMG), and center of pressure (COP) data during mechanical perturbations in participants wearing an exoskeleton.
  • Utilized a convolutional neural network to predict balance perturbations from single-trial EEG.
  • Employed dynamic functional connectivity analysis and GradCAM for model interpretation.
  • Developed a gated recurrent unit model for continuous-time decoding of COP trajectories from EEG.

Main Results:

  • Perturbation Evoked Potentials (PEP) were detected in EEG 75-134 ms post-perturbation, preceding EMG and COP changes.
  • The convolutional neural network achieved a 75.0% F-score in predicting perturbations from EEG.
  • Model explanations confirmed reliance on PEP components, not artifacts.
  • EEG-based decoding of COP trajectories yielded a Pearson's correlation coefficient of 0.7.

Conclusions:

  • EEG signals contain short-latency neural information predictive of impending falls.
  • This neural information precedes physical balance recovery responses.
  • EEG shows potential for developing brain-machine interfaces for fall prevention in exoskeletons.