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.
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.
Authors: Mónica da Silva Cameirão; Sergi Bermúdez I Badia; Esther Duarte; Paul F M J Verschure Journal: Restor Neurol Neurosci Date: 2011 Impact factor: 2.406
Authors: Karin Brütsch; Tabea Schuler; Alexander Koenig; Lukas Zimmerli; Susan Mérillat -Koeneke; Lars Lünenburger; Robert Riener; Lutz Jäncke; Andreas Meyer-Heim Journal: J Neuroeng Rehabil Date: 2010-04-22 Impact factor: 4.262
Authors: G Cheron; M Duvinage; C De Saedeleer; T Castermans; A Bengoetxea; M Petieau; K Seetharaman; T Hoellinger; B Dan; T Dutoit; F Sylos Labini; F Lacquaniti; Y Ivanenko Journal: Neural Plast Date: 2012-01-04 Impact factor: 3.599
Authors: Rahul Goel; Recep A Ozdemir; Sho Nakagome; Jose L Contreras-Vidal; William H Paloski; Pranav J Parikh Journal: Exp Brain Res Date: 2018-05-11 Impact factor: 1.972