Improving upper limb motor function in chronic stroke patients using a brain-computer interface system based on motor imagery combined with different end effectors:a preliminary study
Objective:To preliminarily compare the effectiveness of different end-effectors on upper limb motor function im-provement in chronic stroke patients undergoing motor imagery-based brain-computer interface(BCI) rehabilitation. Method:Thirty-two chronic stroke patients who received motor imagery-based BCI rehabilitation were includ-ed. Based on the BCI training received,patients were divided into two groups:BCI combined with virtual re-ality feedback and BCI combined with wrist joint robot feedback. Assessment of upper limb motor function (upper limb Fugl-Meyer assessment),active range of motion of the paralyzed wrist,surface electromyography reflecting active contraction of wrist extensor muscles,and motor-evoked potentials (MEP) were conducted be-fore and after intervention. Thus,to compare the impact of virtual reality feedback and wrist joint robot feed-back on upper limb motor function recovery in chronic stroke patients.Result:Prior to intervention,baseline results were comparable between the two groups(P=0.160). After inter-vention,significant improvements in upper limb motor function were observed in both groups (P<0.001). The wrist joint robot feedback group showed a trend significant improvement in Fugl-Meyer scores and the changes in Fugl-Meyer scores compared to the virtual reality feedback group (P=0.06). Regarding active range of mo-tion of the paralyzed wrist,both groups initially lacked active flexion-extension movements,and after interven-tion,the improvement was 33.13°±24.96° in the wrist joint robot group and 27.81°±37.17° in the virtual reali-ty group,with no significant difference between the two groups (P>0.638). In the assessment of surface elec-tromyography for wrist extensor muscle group on the paralyzed side,both groups showed significant improve-ment in average amplitude and area under the curve after intervention (P<0.001),with the wrist joint robot group demonstrating a more significant improvement in average amplitude(P<0.001),while area under curve showed a trend of improvement. In the MEP evaluation,the elicitation rate after intervention was 56.25％ in the wrist joint robot group and 18.75％ in the virtual reality group. Conclusion:Motor imagery-based BCI rehabilitation systems effectively improve upper limb motor function in chronic stroke patients. Wrist joint robot feedback appears to be more significant in improvement compared to virtual reality feedback. Integrating sensory and motor information might better affect upper limb motor recov-ery. These might help to optimize the feedback strategy of BCI training.
万方数据
维普
