期刊
IEEE TRANSACTIONS ON NEURAL SYSTEMS AND REHABILITATION ENGINEERING
卷 30, 期 -, 页码 1737-1744出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TNSRE.2022.3185262
关键词
Training; Robots; Soft robotics; Electroencephalography; Stroke (medical condition); Robot kinematics; Medical treatment; Brain computer interfaces (BCI); soft robotic glove; steady-state visually evoked potentials (SSVEP); hand rehabilitation; post stroke
资金
- Shenzhen-Hong Kong-Macau Technology Research Programme [SGDX2019081623201196]
- Shenzhen Local Science and Technology Development Fund guided by Central Government of China [2021Szvup130]
- Science and Technology Development Fund, Macau SAR [055/2015/A2, 0045/2019/AFJ, 0018/2019/AKP]
This study explored the use of a soft robotic glove controlled by a brain-computer interface (BCI) for post-stroke hand function rehabilitation. The results showed that the BCI-controlled robotic glove, based on steady-state visually evoked potentials (SSVEP), significantly improved hand function after rehabilitation. The efficacy of this new paradigm was comparable to previous studies using motor imagery-based BCI robotic hand rehabilitation.
Soft robotic glove with brain computer interfaces (BCI) control has been used for post-stroke hand function rehabilitation. Motor imagery (MI) based BCI with robotic aided devices has been demonstrated as an effective neural rehabilitation tool to improve post-stroke hand function. It is necessary for a user of MI-BCI to receive a long time training, while the user usually suffers unsuccessful and unsatisfying results in the beginning. To propose another non-invasive BCI paradigm rather than MI-BCI, steady-state visually evoked potentials (SSVEP) based BCI was proposed as user intension detection to trigger the soft robotic glove for post-stroke hand function rehabilitation. Thirty post-stroke patients with impaired hand function were randomly and equally divided into three groups to receive conventional, robotic, and BCI-robotic therapy in this randomized control trial (RCT). Clinical assessment of Fugl-Meyer Motor Assessment of Upper Limb (FMA-UL), Wolf Motor Function Test (WMFT) and Modified Ashworth Scale (MAS) were performed at pre-training, post-training and three months follow-up. In comparing to other groups, The BCI-robotic group showed significant improvement after training in FMA full score (10.05 +/- 8.03, p = 0.001), FMA shoulder/elbow (6.2 +/- 5.94, p = 0.0004) and FMA wrist/hand (4.3 +/- 2.83, p = 0.007), and WMFT (5.1 +/- 5.53, p = 0.037). The improvement of FMA was significantly correlated with BCI accuracy (r = 0.714, p = 0.032). Recovery of hand function after rehabilitation of SSVEP-BCI controlled soft robotic glove showed better result than solely robotic glove rehabilitation, equivalent efficacy as results from previous reported MI-BCI robotic hand rehabilitation. It proved the feasibility of SSVEP-BCI controlled soft robotic glove in post-stroke hand function rehabilitation.
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