Abstract
BACKGROUND: Non-invasive brain-computer interface (BCI) technology has been widely studied for enabling individuals with spinal cord injury (SCI) to control assistive robotic devices, such as lower-limb exoskeletons. Although BCI-based control of lower-limb exoskeletons has been investigated in individuals with SCI, comparisons with conventional interfaces, such as smartwatches, remain limited. In this study, we developed a BCI system that enables individuals with SCI to control a lower-limb exoskeleton through voluntary walking-related motor imagery. METHODS: This study involved 5 individuals with SCI performing a lower-limb exoskeleton control based on the BCI and a smartwatch as a conventional controller. For the BCI, the participants control the exoskeleton by gait-related MI tasks, including imagining walking forward and sitting down. EEG signals were recorded and processed through a session-transfer approach based on a dual-domain convolutional neural network. To compare the BCI and the smartwatch, all participants performed the exoskeleton control experiments in the same course and completed a usability evaluation. RESULTS: Experimental results showed that participants perceived our BCI system as more practical and user-friendly than a smartwatch for controlling the exoskeleton during crutch-assisted walking. A usability evaluation conducted after the real-time control experiment showed that the BCI system was perceived as more satisfactory than the smartwatch. CONCLUSIONS: This study suggests that the MI-BCI system may offer greater practicality and stability compared to smartwatch-based control for lower limb exoskeleton in individuals with SCI. Consequently, our results may not only enhance the quality of life for individuals with SCI but also broaden the potential for developing BCI applications in real-world environments.