Gait adaptation to visual kinematic perturbations using a real-time closed-loop brain–computer interface to a virtual reality avatar. (11th April 2016)

• Record Type: Journal Article
• Authors: Luu, Trieu Phat; He, Yongtian; Brown, Samuel; Nakagome, Sho; Contreras-Vidal, Jose L
• Is Part Of: Journal of neural engineering. Volume 13:Number 3(2016:Jun.)
• Publication Date: 2016-04-11
• Subjects: brain computer interfaces -- visuomotor adaptation -- gait adaptation -- BCI-VR systems; Neurosciences -- Periodicals; Biomedical engineering -- Periodicals; 612.8
• DOI: 10.1088/1741-2560/13/3/036006
• Languages: English
• ISSNs: 1741-2560

Abstract

Abstract: Objective. 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. Approach . 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. Main results . 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. Significance . 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.