《中国康复理论与实践》 ›› 2025, Vol. 31 ›› Issue (6): 703-710.doi: 10.3969/j.issn.1006-9771.2025.06.011
刘璇1,2, 高玲1,2, 褚凤明1,2, 陈杰1,2, 张明1,2,3()
收稿日期:
2025-01-20
修回日期:
2025-04-08
出版日期:
2025-06-25
发布日期:
2025-06-16
通讯作者:
张明(1985-),男,硕士,副教授、主任治疗师,硕士研究生导师。E-mail:作者简介:
刘璇(2001-),女,汉族,江苏徐州市人,硕士研究生,主要研究方向:神经康复。
基金资助:
LIU Xuan1,2, GAO Ling1,2, CHU Fengming1,2, CHEN Jie1,2, ZHANG Ming1,2,3()
Received:
2025-01-20
Revised:
2025-04-08
Published:
2025-06-25
Online:
2025-06-16
Contact:
E-mail: Supported by:
摘要:
目的 观察基于运动表象的脑机接口(BCI)联合上肢康复机器人对脑卒中患者上肢功能的影响。
方法 2024年5月至12月,选取徐州医科大学附属徐州康复医院和徐州市中心医院住院的脑卒中患者45例,随机分为BCI组(n = 15)、机器人组(n = 15)和联合组(n = 15)。3组均接受常规康复治疗,BCI组增加基于运动表象的BCI治疗,机器人组增加机器人治疗,联合组增加BCI和机器人治疗。治疗前和治疗4周后分别采用Fugl-Meyer 评定量表上肢部分(FMA-UE)、上肢动作研究量表(ARAT)和改良Barthel指数(MBI)进行评定,采用定量脑电图记录平均头皮δ-α比(DAR)。
结果 FMA-UE、ARAT、MBI和DAR评分组内效应(F > 101.870, P < 0.001)和交互效应(F > 7.891, P < 0.001)均显著,事后两两比较显示,联合组各项指标均优于BCI组和机器人组(P < 0.05),BCI组和机器人组各项指标无显著性差异(P > 0.05)。
结论 BCI联合上肢康复机器人能促进脑卒中患者上肢功能的恢复,提高日常生活活动能力。
中图分类号:
刘璇, 高玲, 褚凤明, 陈杰, 张明. 脑机接口联合上肢康复机器人对脑卒中患者上肢功能的影响[J]. 《中国康复理论与实践》, 2025, 31(6): 703-710.
LIU Xuan, GAO Ling, CHU Fengming, CHEN Jie, ZHANG Ming. Effect of brain-computer interface combined with upper limb rehabilitation robot on upper limb function of stroke patients[J]. Chinese Journal of Rehabilitation Theory and Practice, 2025, 31(6): 703-710.
表2
各组治疗前后各指标的描述性统计结果"
变量 | 组别 | 治疗前 | 治疗后 | ||
---|---|---|---|---|---|
均值 | 标准差 | 均值 | 标准差 | ||
FMA-UE | 脑机接口组 | 28.00 | 3.85 | 33.80 | 3.82 |
机器人组 | 27.73 | 5.50 | 33.40 | 5.60 | |
联合组 | 25.87 | 6.55 | 38.93 | 8.56 | |
ARAT | 脑机接口组 | 10.20 | 4.46 | 16.07 | 4.17 |
机器人组 | 11.67 | 5.86 | 15.93 | 5.48 | |
联合组 | 12.13 | 5.25 | 22.67 | 5.43 | |
MBI | 脑机接口组 | 54.40 | 10.95 | 60.27 | 10.26 |
机器人组 | 52.53 | 9.53 | 59.80 | 9.14 | |
联合组 | 56.27 | 6.95 | 67.27 | 7.08 | |
DAR | 脑机接口组 | 3.22 | 1.17 | 2.51 | 0.99 |
机器人组 | 3.12 | 0.77 | 2.57 | 0.80 | |
联合组 | 3.30 | 1.33 | 1.71 | 0.93 |
表3
各指标的重复测量方差分析结果"
变量 | 平方和 | 自由度 | 均方 | F值 | P值 | |
---|---|---|---|---|---|---|
FMA-UE | 组内 | 1504.711 | 1 | 1504.711 | 342.846 | < 0.001 |
组间 | 57.222 | 2 | 28.611 | 0.443 | 0.645 | |
组内×组间 | 268.956 | 2 | 134.478 | 30.641 | < 0.001 | |
ARAT | 组内 | 953.878 | 1 | 953.878 | 336.851 | < 0.001 |
组间 | 183.267 | 2 | 91.633 | 1.683 | 0.198 | |
组内×组间 | 44.689 | 2 | 22.344 | 7.891 | < 0.001 | |
MBI | 组内 | 1456.044 | 1 | 1456.044 | 662.316 | < 0.001 |
组间 | 523.756 | 2 | 261.878 | 1.599 | 0.214 | |
组内×组间 | 105.622 | 2 | 52.811 | 24.022 | < 0.001 | |
DAR | 组内 | 20.268 | 1 | 20.268 | 101.870 | < 0.001 |
组间 | 2.466 | 2 | 1.233 | 0.657 | 0.523 | |
组内×组间 | 4.756 | 2 | 2.378 | 11.953 | < 0.001 |
表4
各组治疗前后的差异性检验结果"
变量 | 组别 | 治疗前后平均值差 | 平均值差标准差 | P值 | 95%CI | |
---|---|---|---|---|---|---|
下限 | 上限 | |||||
FMA-UE | 脑机接口组 | -5.800 | 1.821 | < 0.001 | -7.344 | -4.256 |
机器人组 | -5.667 | 1.633 | < 0.001 | -7.210 | -4.123 | |
联合组 | -13.067 | 4.511 | < 0.001 | -14.610 | -11.523 | |
ARAT | 脑机接口组 | -5.867 | 1.995 | < 0.001 | -7.107 | -4.627 |
机器人组 | -5.200 | 1.373 | < 0.001 | -6.440 | -3.960 | |
联合组 | -8.467 | 3.335 | < 0.001 | -9.707 | -7.227 | |
MBI | 脑机接口组 | -5.867 | 1.457 | < 0.001 | -6.959 | -4.774 |
机器人组 | -7.267 | 1.831 | < 0.001 | -8.359 | -6.174 | |
联合组 | -11.000 | 2.777 | < 0.001 | -12.093 | -9.907 | |
DAR | 脑机接口组 | 0.712 | 0.450 | < 0.001 | 0.383 | 1.041 |
机器人组 | 0.543 | 0.249 | 0.002 | 0.215 | 0.872 | |
联合组 | 1.592 | 0.964 | < 0.001 | 1.263 | 1.921 |
表5
各指标的事前LSD差异检验结果"
变量 | 组别 | 组别 | 均值差 | P值 | 95%CI | |
---|---|---|---|---|---|---|
下限 | 上限 | |||||
FMA-UE | 脑机接口组 | 机器人组 | 0.267 | 0.893 | -3.722 | 4.256 |
脑机接口组 | 联合组 | 2.133 | 0.287 | -1.856 | 6.122 | |
机器人组 | 联合组 | 1.867 | 0.350 | -2.122 | 5.856 | |
ARAT | 脑机接口组 | 机器人组 | -0.800 | 0.695 | -4.887 | 3.287 |
脑机接口组 | 联合组 | -1.933 | 0.345 | -6.021 | 2.154 | |
机器人组 | 联合组 | -1.133 | 0.579 | -5.221 | 2.954 | |
MBI | 脑机接口组 | 机器人组 | 1.867 | 0.585 | -4.981 | 8.714 |
脑机接口组 | 联合组 | -1.867 | 0.585 | -8.714 | 4.981 | |
机器人组 | 联合组 | -3.733 | 0.277 | -10.581 | 3.114 | |
DAR | 脑机接口组 | 机器人组 | 0.109 | 0.791 | -0.714 | 0.931 |
脑机接口组 | 联合组 | -0.077 | 0.850 | -0.900 | 0.745 | |
机器人组 | 联合组 | -0.186 | 0.650 | -1.009 | 0.637 |
表6
各指标的事后LSD差异检验结果"
变量 | 组别 | 组别 | 均值差 | P值 | 95%CI | |
---|---|---|---|---|---|---|
下限 | 上限 | |||||
FMA-UE | 脑机接口组 | 机器人组 | 0.400 | 0.863 | -4.245 | 5.045 |
脑机接口组 | 联合组 | -5.133 | 0.031 | -9.779 | -0.488 | |
机器人组 | 联合组 | -5.533 | 0.021 | -10.179 | -0.888 | |
ARAT | 脑机接口组 | 机器人组 | -0.133 | 0.944 | -3.927 | 3.660 |
脑机接口组 | 联合组 | -4.533 | 0.020 | -8.327 | -0.740 | |
机器人组 | 联合组 | -4.400 | 0.024 | -8.193 | -0.607 | |
MBI | 脑机接口组 | 机器人组 | 0.467 | 0.887 | -6.111 | 7.044 |
脑机接口组 | 联合组 | -7.000 | 0.038 | -13.578 | -0.422 | |
机器人组 | 联合组 | -7.467 | 0.027 | -14.044 | -0.889 | |
DAR | 脑机接口组 | 机器人组 | -0.060 | 0.858 | -0.731 | 0.611 |
脑机接口组 | 联合组 | 0.803 | 0.020 | 0.132 | 1.473 | |
机器人组 | 联合组 | 0.863 | 0.013 | 0.192 | 1.533 |
[1] | LI D, CHENG A, ZHANG Z, et al. Effects of low-frequency repetitive transcranial magnetic stimulation combined with cerebellar continuous theta burst stimulation on spasticity and limb dyskinesia in patients with stroke[J]. BMC Neurol, 2021, 21(1): 369. |
[2] |
FEIGIN V L, STARK B A, JOHNSON C O, et al. Global, regional, and national burden of stroke and its risk factors, 1990-2019: a systematic analysis for the Global Burden of Disease Study 2019[J]. Lancet Neurol, 2021, 20(10): 795-820.
doi: 10.1016/S1474-4422(21)00252-0 pmid: 34487721 |
[3] | TU W J, WANG L D, Special Writing Group of China Stroke Surveillance Report. China stroke surveillance report 2021[J]. Mil Med Res, 2023, 10(1): 33. |
[4] | OWOLABI M O, THRIFT A G, MAHAL A, et al. Primary stroke prevention worldwide: translating evidence into action[J]. Lancet Public Health, 2022, 7(1): e74-e85. |
[5] |
LANGHORNE P, COUPAR F, POLLOCK A. Motor recovery after stroke: a systematic review[J]. Lancet Neurol, 2009, 8(8): 741-754.
doi: 10.1016/S1474-4422(09)70150-4 pmid: 19608100 |
[6] | ZHANG R, WANG C, HE S, et al. An adaptive brain-computer interface to enhance motor recovery after stroke[J]. IEEE Trans Neural Syst Rehabil Eng, 2023, 31: 2268-2278. |
[7] |
GAO X, WANG Y, CHEN X, et al. Interface, interaction, and intelligence in generalized brain-computer interfaces[J]. Trends Cogn Sci, 2021, 25(8): 671-684.
doi: 10.1016/j.tics.2021.04.003 pmid: 34116918 |
[8] | CHAI X, CAO T, HE Q, et al. Brain-computer interface digital prescription for neurological disorders[J]. CNS Neurosci Ther, 2024, 30(2): e14615. |
[9] | EVERARD G, DECLERCK L, DETREMBLEUR C, et al. New technologies promoting active upper limb rehabilitation after stroke: an overview and network meta-analysis[J]. Eur J Phys Rehabil Med, 2022, 58(4): 530-548. |
[10] | DEVITTORI G, DINACCI D, ROMITI D, et al. Unsupervised robot-assisted rehabilitation after stroke: feasibility, effect on therapy dose, and user experience[J]. J Neuroeng Rehabil, 2024, 21(1): 52. |
[11] | TERRANOVA T T, SIMIS M, SANTOS A C A, et al. Robot-assisted therapy and constraint-induced movement therapy for motor recovery in stroke: results from a randomized clinical trial[J]. Front Neurorobot, 2021, 15: 684019. |
[12] | YEH I L, HOLST-WOLF J, ELANGOVAN N, et al. Effects of a robot-aided somatosensory training on proprioception and motor function in stroke survivors[J]. J Neuroeng Rehabil, 2021, 18(1): 77. |
[13] |
COSCIA M, WESSEL M J, CHAUDARY U, et al. Neurotechnology-aided interventions for upper limb motor rehabilitation in severe chronic stroke[J]. Brain, 2019, 142(8): 2182-2197.
doi: 10.1093/brain/awz181 pmid: 31257411 |
[14] | 王路遥, 卫倩锐, 张荣, 等. 脑卒中后康复机器人联合多种干预方式的研究进展[J]. 中华物理医学与康复杂志, 2023, 45(6): 568-571. |
[15] | JIA J. Exploration on neurobiological mechanisms of the central-peripheral-central closed-loop rehabilitation[J]. Front Cell Neurosci, 2022, 16: 982881. |
[16] | BANIQUED P D E, STANYER E C, AWAIS M, et al. Brain-computer interface robotics for hand rehabilitation after stroke: a systematic review[J]. J Neuroeng Rehabil, 2021, 18(1): 15. |
[17] | 中华医学会神经病学分会, 中华医学会神经病学分会脑血管病学组. 中国各类主要脑血管病诊断要点2019[J]. 中华神经科杂志, 2019, 52(9): 710-715. |
Chinese Society of Neurology, Chinese Stroke Society. Main diagnostic points of cerebrovascular diseases in China, 2019[J]. Chin J Neurol, 2019, 52(9): 710-715. | |
[18] | LI C, JIANG M, FANG Z, et al. Current evidence of synaptic dysfunction after stroke: cellular and molecular mechanisms[J]. CNS Neurosci Ther, 2024, 30(5): e14744. |
[19] | GNANAPRAKASAM A, KARTHIKBABU S, RAVISHANKAR N, et al. Effect of task-based bilateral arm training on upper limb recovery after stroke: a systematic review and meta-analysis[J]. J Stroke Cerebrovasc Dis, 2023, 32(7): 107131. |
[20] |
VEERBEEK J M, LANGBROEK-AMERSFOORT A C, VAN WEGEN E E H, et al. Effects of robot-assisted therapy for the upper limb after stroke[J]. Neurorehabil Neural Repair, 2017, 31(2): 107-121.
doi: 10.1177/1545968316666957 pmid: 27597165 |
[21] | LIMA J P S, SILVA L A, DELISLE-RODRIGUEZ D, et al. Unraveling transformative effects after tDCS and BCI intervention in chronic post-stroke patient rehabilitation: an alternative treatment design study[J]. Sensors, 2023, 23(23): 9302. |
[22] | MRIDHA M F, DAS S C, KABIR M M, et al. Brain-computer interface: advancement and challenges[J]. Sensors (Basel), 2021, 21(17): 5746. |
[23] | KHAN M A, DAS R, IVERSEN H K, et al. Review on motor imagery based BCI systems for upper limb post-stroke neurorehabilitation: from designing to application[J]. Comput Biol Med, 2020, 123: 103843. |
[24] | FROLOV A A, MOKIENKO O, LYUKMANOV R, et al. Post-stroke rehabilitation training with a motor-imagery-based brain-computer interface (BCI)-controlled hand exoskeleton: a randomized controlled multicenter trial[J]. Front Neurosci, 2017, 11: 400. |
[25] | 刘玲玉, 秦文婷, 靳令经, 等. 基于运动想象的脑机接口在脑卒中后手功能康复中的应用[J]. 中国康复, 2024, 39(12): 707-713. |
LIU L Y, QIN W T, JIN L J, et al. Application of motor imagery-based brain computer interface in stroke patients with hand dysfunction[J]. Chin J Rehabil, 2024, 39(12): 707-713. | |
[26] |
TSUCHIMOTO S, SHINDO K, HOTTA F, et al. Sensorimotor connectivity after motor exercise with neurofeedback in post-stroke patients with hemiplegia[J]. Neuroscience, 2019, 416: 109-125.
doi: S0306-4522(19)30521-4 pmid: 31356896 |
[27] | FLEURY M, LIOI G, BARILLOT C, et al. A survey on the use of haptic feedback for brain-computer interfaces and neurofeedback[J]. Front Neurosci, 2020, 14: 528. |
[28] |
LIU X, ZHANG W, LI W, et al. Effects of motor imagery based brain-computer interface on upper limb function and attention in stroke patients with hemiplegia: a randomized controlled trial[J]. BMC Neurol, 2023, 23: 136.
doi: 10.1186/s12883-023-03150-5 pmid: 37003976 |
[29] | CHEN Y W, LI K Y, LIN C H, et al. The effect of sequential combination of mirror therapy and robot-assisted therapy on motor function, daily function, and self-efficacy after stroke[J]. Sci Rep, 2023, 13(1): 16841. |
[30] |
CALABRÒ R S, ACCORINTI M, PORCARI B, et al. Does hand robotic rehabilitation improve motor function by rebalancing interhemispheric connectivity after chronic stroke? Encouraging data from a randomised-clinical-trial[J]. Clin Neurophysiol, 2019, 130(5): 767-780.
doi: S1388-2457(19)30079-3 pmid: 30904771 |
[31] |
ZHANG L, JIA G, MA J, et al. Short and long-term effects of robot-assisted therapy on upper limb motor function and activity of daily living in patients post-stroke: a meta-analysis of randomized controlled trials[J]. J Neuroeng Rehabil, 2022, 19: 76.
doi: 10.1186/s12984-022-01058-8 pmid: 35864524 |
[32] | SU T, WANG M, CHEN Z, et al. Effect of upper robot-assisted training on upper limb motor, daily life activities, and muscular tone in patients with stroke: a systematic review and meta‐analysis[J]. Brain Behav, 2024, 14(11): e70117. |
[33] |
刘换, 韩雪, 宋佳苧, 等. 体位限制下康复机器人训练对脑卒中后肩关节半脱位患者上肢功能的效果[J]. 中国康复理论与实践, 2024, 30(3): 303-309.
doi: 10.3969/j.issn.1006-9771.2024.03.007 |
LIU H, HAN X, SONG J N, et al. Effect of robotic training under position limitation on upper limbs in patients with shoulder subluxation after stroke[J]. Chin J Rehabil Theory Pract, 2024, 30(3): 303-309. | |
[34] | GUO N, WANG X, DUANMU D, et al. SSVEP-based brain computer interface controlled soft robotic glove for post-stroke hand function rehabilitation[J]. IEEE Trans Neural Syst Rehabil Eng, 2022, 30: 1737-1744. |
[35] | HALME H L, PARKKONEN L. The effect of visual and proprioceptive feedback on sensorimotor rhythms during BCI training[J]. PLoS One, 2022, 17(2): e0264354. |
[36] | QIAN Q, NAM C, GUO Z, et al. Distal versus proximal-an investigation on different supportive strategies by robots for upper limb rehabilitation after stroke: a randomized controlled trial[J]. J Neuroeng Rehabil, 2019, 16(1): 64. |
[37] | NIAZI I K, NAVID M S, RASHID U, et al. Associative cued asynchronous BCI induces cortical plasticity in stroke patients[J]. Ann Clin Transl Neurol, 2022, 9(5): 722-733. |
[38] | SHIRAI H. Hybrid ray-mode analysis of E-polarized plane wave diffraction by a thick slit[J]. IEEE Trans Antennas Propag, 2016, 64(11): 4828-4835. |
[39] | SAID R R, HEYAT M B B, SONG K, et al. A systematic review of virtual reality and robot therapy as recent rehabilitation technologies using EEG-brain-computer interface based on movement-related cortical potentials[J]. Biosensors, 2022, 12(12): 1134. |
[40] | 徐硕, 贾杰. "中枢-外周-中枢"闭环康复:脑卒中后手功能康复新理念的临床应用进展[J]. 中国康复医学杂志, 2024, 39(10): 1537-1541. |
[41] | 张宇佳, 陈健尔. 定量脑电图在脑卒中运动功能障碍康复中的应用进展[J]. 中华物理医学与康复杂志, 2024, 46(2): 172-176. |
[42] |
LANZONE J, MOTOLESE F, RICCI L, et al. Quantitative measures of the resting EEG in stroke: a systematic review on clinical correlation and prognostic value[J]. Neurol Sci, 2023, 44(12): 4247-4261.
doi: 10.1007/s10072-023-06981-9 pmid: 37542545 |
[43] | LIUZZI P, GRIPPO A, SODERO A, et al. Quantitative EEG and prognosis for recovery in post-stroke patients: the effect of lesion laterality[J]. Neurophysiol Clin, 2024, 54(3): 102952. |
[1] | 周天添, 张通, 张琦, 梁艳华, 张燕庆, 岳青, 李思佳. Lokomat机器人辅助步行训练对偏瘫儿童下肢运动功能的效果[J]. 《中国康复理论与实践》, 2025, 31(6): 711-720. |
[2] | 付国军, 余秀芳, 吕鑫, 吉璐, 刘华庆. 复合电磁刺激联合下颌抗阻训练对卒中后吞咽障碍的效果[J]. 《中国康复理论与实践》, 2025, 31(6): 721-728. |
[3] | 梁丹, 王卫宁, 李策, 吴越, 徐舒, 谢鸿宇, 吴毅, 朱玉连. 高压氧舱内同步脑仿生电刺激对脑卒中相关睡眠障碍的效果[J]. 《中国康复理论与实践》, 2025, 31(5): 497-504. |
[4] | 柏敏, 曹丽华, 叶子琦, 周定杰, 李雪萍. 肌电感知机器人辅助训练联合成对关联刺激对脑卒中偏瘫患者上肢功能的影响[J]. 《中国康复理论与实践》, 2025, 31(5): 505-512. |
[5] | 邹聪聪, 王潇珺, 马锦蓉, 鲁商波, 丁勇, 王哈妮, 宋建飞. 耳迷走神经电刺激联合双任务训练对缺血性脑卒中患者上肢功能的效果[J]. 《中国康复理论与实践》, 2025, 31(5): 513-519. |
[6] | 施滨, 徐宁, 周广雪. 镜像疗法应用于脑卒中运动功能康复的文献计量分析[J]. 《中国康复理论与实践》, 2025, 31(5): 561-572. |
[7] | 陈蒙晔, 曲庆明, 朱杰, 陈祥贵, 贾杰. 基于心肺运动试验的脑卒中偏瘫患者心肺适能的特征[J]. 《中国康复理论与实践》, 2025, 31(4): 441-447. |
[8] | 李鑫磊, 魏伟, 宋健, 赵雨晴, 孔维橙, 蔡嘉玉, 施浩然, 薛偕华. 静息态脑电图在脑卒中患者上肢运动功能评估中的应用[J]. 《中国康复理论与实践》, 2025, 31(4): 448-457. |
[9] | 刘鹏程, 屈萌艰, 龙黎萍, 王亚琳, 阳明珠, 刘培勇, 周君, 刘静. 多重感觉刺激模态的气电手训练系统联合低频重复经颅磁刺激对脑卒中患者手部运动和触压觉的效果[J]. 《中国康复理论与实践》, 2025, 31(4): 458-465. |
[10] | 苏盼盼, 叶朋, 卢倩, 何川, 陆晓. 视觉剥夺训练联合本体感觉训练对脑卒中偏瘫患者平衡功能的效果[J]. 《中国康复理论与实践》, 2025, 31(3): 254-263. |
[11] | 林昌盛, 曹妤, 王彤, 戴文俊, 侯红, 胡翠琴, 包士雷, 庞素芳. 闭链运动训练对脑卒中偏瘫肩痛和肩关节稳定性的效果:基于超声的评定[J]. 《中国康复理论与实践》, 2025, 31(3): 264-273. |
[12] | 耿文慧, 周严红, 尹俊普, 韩磊, 高阳. 乳腺癌术后肩关节活动度分析[J]. 《中国康复理论与实践》, 2025, 31(3): 356-364. |
[13] | 王潇珺, 王哈妮, 俞红, 李元梅, 周煜达. 高精度经颅直流电刺激联合上肢机器人对缺血性脑卒中上肢功能的效果[J]. 《中国康复理论与实践》, 2025, 31(2): 218-224. |
[14] | 马雯雯, 温嬿峥, 满日帕提·肉孜, 崔博雅, 苏音其梅. 健侧倾斜训练对脑卒中后Pusher综合征患者平衡功能的效果[J]. 《中国康复理论与实践》, 2025, 31(2): 225-230. |
[15] | 秦晴, 刘叶, 叶海燕, 李晨, 陈迪. 上肢机器人辅助干预脑卒中的文献计量分析[J]. 《中国康复理论与实践》, 2025, 31(1): 85-98. |
阅读次数 | ||||||
全文 |
|
|||||
摘要 |
|
|||||
|