中国科学院自动化研究所机构知识库

Motor imagery based brain-computer interface (MI-BCI) has
been extensively researched as a potential intervention to enhance motor function for post-stroke patients. However, the difficulties in performing imagery tasks and the constrained spatial resolution of electroencephalography complicate the decoding of fine motor imagery (MI).
To overcome the limitation, an enhanced MI-BCI rehabilitation system
based on vibration stimulation and robotic glove is proposed in this paper. First, a virtual scene involving object-oriented palmar grasping and
pinching actions, is designed to enhance subjects’ engagement in performing MI tasks by providing straightforward and specific goals. Then,
vibration stimulation, which can offer proprioceptive feedback, is introduced to help subjects better switch their attention to the corresponding MI limbs. Finally, the self-designed pneumatic manipulator control
module is developed for motion execution based on the MI classification
results. Seven healthy individuals were recruited to validate the feasibility of the system in improving subjects’ MI abilities. The results show
that the classification accuracy of three-class fine MI can be improved
to 65.67%, which is significantly higher than the state-of-the art studies. This demonstrates the great potential of the proposed system in the
application of post-stroke rehabilitation training.