gms | German Medical Science

Background: Impaired hand function in stroke survivors hampers performing daily functional tasks independently. Conventional rehabilitation equipment such as passive motion device cannot allow patients to play an active role in performing the hand exercises. Therefore, there is a strong need for an assistive device that resolves the lack of compliant movement-assisted hand motion and facilitates intuitive user control.

Aim: This research aims to assess the efficacy of a novel BCI-controlled soft robotic glove (BCI-RG) in assisting stroke patients with completing functional tasks. We hypothesize that BCI-RG will benefit the patients more than conventional passive motion robotic glove (Passive-RG) as it allows the patients to use their brainwave signals to control the glove directly with bypassing the stroke-afflicted arm.

Method: Eleven patients are randomly assigned to BCI-RG and Passive-RG group to participate in 18 sessions over 6 weeks. For BCI-RG, a participant wears an EEG cap and robotic glove and watches short videos, e.g., grasp a cup and move it. The participant imagines such a move and the brain signal will be recorded as a reference. When the participant pictures this move again, the glove is actuated automatically to grasp the cup. Passive-RG is actuated by preprogramed sequences. The outcome measures including Action Research Arm Test, Grip and Pinch Strength, Fugl-Meyer Assessment are assessed in week 0, 6, 12, and 24.

Results/findings: work in progress

Discussion and conclusions: Though there is no difference in finger strength compared with passive-RG group, intensive BCI training with robotic glove achieves significant functional recovery and even amplifies the effect over time. This may suggest motor imagery led to subsequent finger movement formulate novel neural circuits and engrams which perpetuate functional improvement via spontaneous learning process.