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

Akshay Sujatha Ravindran, Christopher A. Malaya, Isaac John, Gerard E. Francisco, Charles Layne, Jose L. Contreras-Vidal

Research output: Contribution to journalArticlepeer-review

4 Scopus citations


Objective: Falls are a leading cause of death in adults 65 and older. Recent efforts to restore lower-limb function in these populations have seen an increase in the use of wearable robotic systems; however, fall prevention measures in these systems require early detection of balance loss to be effective. Prior studies have investigated whether kinematic variables contain information about an impending fall, but few have examined the potential of using electroencephalography (EEG) as a fall-predicting signal and how the brain responds to avoid a fall. Approach: To address this, we decoded neural activity in a balance perturbation task while wearing an exoskeleton. We acquired EEG, electromyography (EMG), and center of pressure (COP) data from seven healthy participants during mechanical perturbations while standing. The timing of the perturbations was randomized in all trials. Main results: We found perturbation evoked potentials (PEP) components as early as 75-134 ms after the onset of the external perturbation, which preceded both the peak in EMG ( 1/4180 ms) and the COP ( 1/4350 ms). A convolutional neural network trained to predict balance perturbations from single-trial EEG had a mean F-score of 75.0 ± 4.3 % . Clustering GradCAM-based model explanations demonstrated that the model utilized components in the PEP and was not driven by artifacts. Additionally, dynamic functional connectivity results agreed with model explanations; the nodal connectivity measured using phase difference derivative was higher in the occipital-parietal region in the early stage of perturbations, before shifting to the parietal, motor, and back to the frontal-parietal channels. Continuous-time decoding of COP trajectories from EEG, using a gated recurrent unit model, achieved a mean Pearson's correlation coefficient of 0.7 ± 0.06. Significance: Overall, our findings suggest that EEG signals contain short-latency neural information related to an impending fall, which may be useful for developing brain-machine interface systems for fall prevention in robotic exoskeletons.

Original languageEnglish (US)
Article number036015
JournalJournal of neural engineering
Issue number3
StatePublished - May 26 2022


  • CNN
  • deep learning
  • EEG
  • exoskeleton
  • fall prevention
  • interpretability
  • perturbation evoked potential
  • Brain-Computer Interfaces
  • Electroencephalography/methods
  • Humans
  • Deep Learning
  • Exoskeleton Device
  • Adult
  • Electromyography

ASJC Scopus subject areas

  • Cellular and Molecular Neuroscience
  • Biomedical Engineering


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