title: Gait adaptation to visual kinematic perturbations using a real-time closed-loop brain-computer interface to a virtual reality avatar.
abstract: The control of human bipedal locomotion is of great interest to the field of lower-body brain-computer interfaces (BCIs) for gait rehabilitation. While the feasibility of closed-loop BCI systems for the control of a lower body exoskeleton has been recently shown, multi-day closed-loop neural decoding of human gait in a BCI virtual reality (BCI-VR) environment has yet to be demonstrated. BCI-VR systems provide valuable alternatives for movement rehabilitation when wearable robots are not desirable due to medical conditions, cost, accessibility, usability, or patient preferences.
  
  
  
  In this study, we propose a real-time closed-loop BCI that decodes lower limb joint angles from scalp electroencephalography (EEG) during treadmill walking to control a walking avatar in a virtual environment. Fluctuations in the amplitude of slow cortical potentials of EEG in the delta band (0.1-3 Hz) were used for prediction; thus, the EEG features correspond to time-domain amplitude modulated potentials in the delta band. Virtual kinematic perturbations resulting in asymmetric walking gait patterns of the avatar were also introduced to investigate gait adaptation using the closed-loop BCI-VR system over a period of eight days.
  
  
  
  Our results demonstrate the feasibility of using a closed-loop BCI to learn to control a walking avatar under normal and altered visuomotor perturbations, which involved cortical adaptations. The average decoding accuracies (Pearson's r values) in real-time BCI across all subjects increased from (Hip: 0.18 ± 0.31; Knee: 0.23 ± 0.33; Ankle: 0.14 ± 0.22) on Day 1 to (Hip: 0.40 ± 0.24; Knee: 0.55 ± 0.20; Ankle: 0.29 ± 0.22) on Day 8.
  
  
  
  These findings have implications for the development of a real-time closed-loop EEG-based BCI-VR system for gait rehabilitation after stroke and for understanding cortical plasticity induced by a closed-loop BCI-VR system.
authors.forename: Trieu Phat
authors.initials: TP
authors.lastname: Luu
authors.forename: Yongtian
authors.initials: Y
authors.lastname: He
authors.forename: Samuel
authors.initials: S
authors.lastname: Brown
authors.forename: Sho
authors.initials: S
authors.lastname: Nakagame
authors.forename: Jose L
authors.initials: JL
authors.lastname: Contreras-Vidal
mesh_terms: D000222:Adaptation, Physiological / Q000502:physiology*; D000328:Adult; D000465:Algorithms; D001696:Biomechanical Phenomena / Q000502:physiology*; D062207:Brain-Computer Interfaces; D003199:Computer Systems; D004569:Electroencephalography; D005684:Gait / Q000502:physiology*; D006801:Humans; D007092:Imagination / Q000502:physiology; D008297:Male; D009044:Motor Cortex / Q000502:physiology; D010775:Photic Stimulation; D011597:Psychomotor Performance / Q000502:physiology; D000071939:Stroke Rehabilitation / Q000379:methods; D000076142:Virtual Reality; D016138:Walking / Q000502:physiology; D055815:Young Adult