Abstract
Recent advances in non-invasive brain-computer interface (BCI) technologies have shown the feasibility of neural decoding for both users’ gait intent and continuous kinematics. However, the dynamics of cortical involvement in human upright walking with a closed-loop BCI has not been investigated. This study aims to investigate the changes of cortical involvement in human treadmill walking with and without BCI control of a walking avatar. Source localization revealed significant differences in cortical network activity between walking with and without closed-loop BCI control. Our results showed sustained α/µ suppression in the Posterior Parietal Cortex and Inferior Parietal Lobe, indicating increases of cortical involvement during walking with BCI control. We also observed significant increased activity of the Anterior Cingulate Cortex (ACC) in the low frequency band suggesting the presence of a cortical network involved in error monitoring and motor learning. Additionally, the presence of low γ modulations in the ACC and Superior Temporal Gyrus may associate with increases of voluntary control of human gait. This work is a further step toward the development of a novel training paradigm for improving the efficacy of rehabilitation in a top-down approach.
Highlights
Non-invasive scalp electroencephalogram (EEG) has recently been used to monitor cortical activities during human walking because of its portability and high time resolution[1, 2]
We hypothesized that the closed-loop EEG-based Brain-Computer Interface (BCI)-virtual reality (VR) system enhances cortical involvement in human treadmill walking, triggers cortical networks involved in error monitoring and motor learning, and increases voluntary control of human gait
Our results showed significant increases in cortical activity in the low frequency (Δ bands) in the Anterior Cingulate Cortex (ACC) area suggesting the possible benefit of using a closed-loop BCI-VR that recruits cortical network involved in error monitoring and motor learning
Summary
Non-invasive scalp electroencephalogram (EEG) has recently been used to monitor cortical activities during human walking because of its portability and high time resolution[1, 2]. VR has been explored in a few walking-related BCI systems[19,20,21] It provides real-time feedback to BCI users, while avoiding many technical challenges that lower-limb robotics face, such as high cost, risk of falls, and joint misalignment. We hypothesized that the closed-loop EEG-based BCI-VR system enhances cortical involvement in human treadmill walking, triggers cortical networks involved in error monitoring and motor learning, and increases voluntary control of human gait. Once confirmed, it will have important implications in transferring BCI-based protocols to gait rehabilitation
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