Gait adaptation to visual kinematic perturbations using a real-time closed-loop brain-computer interface to a virtual...

Area of Science:

• Neuroscience
• Rehabilitation Engineering
• Biomedical Engineering

Background:

• Human bipedal locomotion control is crucial for lower-body brain-computer interfaces (BCIs) in gait rehabilitation.
• While closed-loop BCI systems for exoskeleton control are feasible, multi-day neural decoding for gait in a BCI virtual reality (BCI-VR) environment remains undemonstrated.
• BCI-VR offers an alternative rehabilitation approach when wearable robots are not suitable.

Purpose of the Study:

• To develop and evaluate a real-time, closed-loop BCI system for decoding lower limb joint angles from EEG during treadmill walking.
• To control a virtual avatar's gait in a BCI-VR environment.
• To investigate gait adaptation and cortical plasticity using the BCI-VR system over an eight-day period with virtual kinematic perturbations.

Main Methods:

• A closed-loop BCI system was implemented to decode lower limb joint angles from scalp EEG during treadmill walking.
• EEG features, specifically fluctuations in delta band (0.1-3 Hz) slow cortical potentials, were used for real-time prediction.
• Virtual kinematic perturbations were introduced to induce asymmetric gait patterns and study adaptation within the BCI-VR system.

Main Results:

• The study demonstrated the feasibility of a closed-loop BCI for controlling a walking avatar under normal and perturbed conditions, indicating cortical adaptation.
• Real-time decoding accuracies (Pearson's r) for hip, knee, and ankle joints significantly improved from Day 1 to Day 8.
• Average decoding accuracies 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.

Conclusions:

• Findings support the feasibility of a real-time, closed-loop EEG-based BCI-VR system for gait rehabilitation.
• The system facilitates learning to control a virtual avatar and adapt to altered visuomotor feedback.
• This research contributes to understanding cortical plasticity induced by closed-loop BCI-VR interventions, particularly for post-stroke recovery.