Abstract
Stroke patients often suffer from poor motor function recovery due to a lack of good rehabilitation training. And hand function impairments especially affect daily life. To enable stroke patients to perform rehabilitation training safely and effectively on their own, we have designed a soft pneumatic robotic system for application in active hand rehabilitation. The soft pneumatic glove in the system can safely lead passive movements with the hand. After training, the user can control the soft pneumatic glove to perform hand rehabilitation tasks by recognizing movement intentions through motor imagery brain-computer interface (MI-BCI). An experiment was designed to compare the rehabilitation performance of four healthy subjects under the visual-based rehabilitation task (VRT) and tactile-based rehabilitation task (TRT). Two subjects had improved online classification accuracy in TRT. Besides, the addition of the vibration stimuli resulted in stronger and long-lasting event-related desynchronization (ERD) than VRT in the sensorimotor cortex during the rehabilitation tasks. These results suggest that our hand rehabilitation system can effectively perform active rehabilitation tasks according to the user’s intention while ensuring safety and efficiency. The addition of vibration stimulation enhances cortical activation during the rehabilitation exercise, improving the efficiency and effectiveness of the rehabilitation. It also has the potential to improve the accuracy of the online classification of MI.
Supported by Basic Research Program of Jiangsu Province (BK201900240).
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Zhang, W., Song, A., Lai, J. (2023). Motor Imagery BCI-Based Online Control Soft Glove Rehabilitation System with Vibrotactile Stimulation. In: Tanveer, M., Agarwal, S., Ozawa, S., Ekbal, A., Jatowt, A. (eds) Neural Information Processing. ICONIP 2022. Communications in Computer and Information Science, vol 1792. Springer, Singapore. https://doi.org/10.1007/978-981-99-1642-9_39
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