Abstract：Cerebral apoplexy has become the leading cause of adult disability in China. Spastic paralysis after stroke seriously affects the motor function and self-care ability of patients, so effective rehabilitation methods are urgently needed. In recent years, brain-computer interface (BCI) technology has provided a new scheme for post-stroke sports rehabilitation training. Compared with BCI based on single mode scalp electroencephalography, BCI based on brain-myoelectric combination can more comprehensively reflect the neural process from the generation of motor intention to behavior control, and also provides a possibility to solve the problems such as low recognition accuracy and few pattern categories of traditional BCI system, and provides a new idea for the rehabilitation of patients with movement disorders. In this paper, we reviewed the combined feature analysis and extraction algorithms of EEG, myography and EEG, introduced the comprehensive evaluation method of neuromuscular system based on the principle of EEG coherence, summarized its rehabilitation training methods combined with active exoskeleton and functional electrical stimulation, and further discussed the challenges and difficulties in this field and predicted its future development trend, aiming to promote in-depth research and development of the cerebral myoelectric coherence method in the field of sports rehabilitation.
王语鹏, 姚远, 雷海霞, 曲睿昊, 王坤, 许敏鹏. 脑肌电联合识别在脑卒中后运动康复中的应用进展[J]. 中国生物医学工程学报, 2025, 44(3): 335-344.
Wang Yupeng, Yao Yuan, Lei Haixia, Qu Ruihao, Wang Kun, Xu Minpeng. Progress in Application of EEG Combined Recognition in Exercise Rehabilitation after Stroke. Chinese Journal of Biomedical Engineering, 2025, 44(3): 335-344.
[1] Wolfe CDA. The impact of stroke[J]. British Medical Bulletin, 2000, 56(2): 275-286. [2] Nakamura S, Tsubokawa T, Yamamoto T, et al. Spinal cord stimulation for post-apoplectic spastic hemiplegia[J]. Neurological Surgery, 1984, 12(4): 485-491. [3] Maloney SJ, Fletcher IM. Lower limb stiffness testing in athletic performance: a critical review[J]. Sports Biomechanics, 2018, 20(1), 109-130. [4] Li Sheng, Francisco GE, Rymer WZ. A new definition of poststroke spasticity and the interference of spasticity with motor recovery from acute to chronic stages[J]. Neurorehabilitation and Neural Repair, 2021, 35(7): 601-610. [5] 何庆华, 彭承琳, 吴宝明. 脑机接口技术研究方法[J]. 重庆大学学报: 自然科学版, 2002, 25(12): 106-109. [6] Khademi Z, Ebrahimi F, Kordy HM. A review of critical challenges in MI-BCI: from conventional to deep learning methods[J]. Journal of Neuroscience Methods, 2023, 383: 109736. [7] 郑杨,王刚,徐进,等. 大脑皮层-肌肉交互作用与注意力关系的实验研究[J].西安交通大学学报, 2014, 48(6):134-138. [8] Pfurtscheller G, Da Silva FHL. Event-related EEG/MEG synchronization and desynchronization: basic principles[J]. Clinical Neurophysiology, 1999, 110(11): 1842-1857. [9] Pineda JA, Allison BZ, Vankov A. The effects of self-movement observation and imagination on mu rhythms and readiness potentials (RP's): toward a brain-computer in terface (BCI)[J]. IEEE Transactions on Rechabilitation Engineering, 2000, 8(2): 219-222. [10] Tong Kaiyu, Ho SK, Pang PMK, et al. An intention driven hand functions task training robotic system[C]//2010 Annual International Conference of the IEEE Engineering in Medicine and Biology. Buenos Aires: IEEE, 2010: 3406-3409. [11] Pfurtscheller G, Müller GR, Pfurtscheller J, et al. ‘Thought’-control of functional electrical stimulation to restore hand grasp in a patient with tetraplegia[J]. Neuroscience Letters, 2003, 351(1): 33-36. [12] Ang KK, Chin ZY, Zhang H, et al. Filter bank common spatial pattern (FBCSP) in brain-computer interface[C]//2008 IEEE International Joint Conference on Neural Networks (IEEE World Congress on Computational Intelligence). Hong Kong: IEEE, 2008: 2390-2397. [13] Alturki FA, AlSharabi K, Abdurraqeeb AM, et al. EEG signal analysis for diagnosing neurological disorders using discrete wavelet transform and intelligent techniques[J]. Sensors, 2020, 20(9): 2505. [14] Xu Yilu, Wei Qingguo, Zhang Hua, et al. Transfer learning based on regularized common spatial patterns using cosine similarities of spatial filters for motor-imagery BCI[J]. Journal of Circuits, Systems and Computers, 2019, 28(7): 1950123. [15] Hinterberger T, Walter N, Doliwa C, et al. The brain's resonance with breathing-decelerated breathing synchronizes heart rate and slow cortical potentials[J]. Journal of Breath Research, 2019, 13(4): 046003. [16] Liao Xiang, Yao Dezhong, Wu Dan. Combining spatial filters for the classification of single-trail EEG in a finger movement task[J]. IEEE Transactions on Biomedical Engineeringm, 2007, 54(5): 821-831. [17] 许敏鹏,魏泽,明东. 基于脑卒中后运动康复领域的运动想象的研究[J]. 生物医学工程学杂志,2020,37(1):169-173. [18] Simpson EK, Ramirez NM, Branstetter B, et al. Occupational therapy practitioners' perspectives of mental health practices with clients in stroke rehabilitation[J]. OTJR: Occupation, Participation and Health, 2018, 38(3): 181-189. [19] Foysal KMR, Baker SN. A hierarchy of corticospinal plasticity in human hand and forearm muscles[J]. The Journal of Physiology, 2019, 597(10): 2729-2739. [20] Kim H, Yoo EY, Jung MY, et al. The effects of mental practice combined with modified constraint-induced therapy on corticospinal excitability, movement quality, function, and activities of daily living in persons with stroke[J]. Disability and Rehabilitation, 2018, 40(20): 2449-2457. [21] Fründt O, Schulz R, Schöttle D, et al. White matter microstructure of the human mirror neuron system is related to symptom severity in adults with autism[J]. Journal of Autism and Developmental Disorders, 2018, 48: 417-429. [22] Zhang Yi, Dai Lingling, Luo Yuan, et al. Design of a surface EMG based human-machine interface for an intelligent wheelchair[C]//IEEE 2011 10th International Conference on Electronic Measurement & Instruments. IEEE, 2011, 3: 132-136. [23] Padmanabhan P, Puthusserypady S. Nonlinear analysis of EMG signals-a chaotic approach[C]//The 26th Annual International Conference of the IEEE Engineering in Medicine and Biology Society. San Francisco: IEEE, 2004, 1: 608-611. [24] Li Ke, Wei Na, Cheng Mei, et al. Dynamical coordination of hand intrinsic muscles for precision grip in diabetes mellitus[J]. Scientific Reports, 2018, 8(1): 4365. [25] Marri K, Navaneetha KM, Jose J, et al. Analysis of fatigue conditions in triceps brachii muscle using sEMG signals and spectral correlation density function[C]//2014 International Conference on Informatics, Electronics & Vision. Dhaka: ICIEV, 2014: 1-4. [26] Zhang Yongqing, Chen Siyu, Cao Wenpeng, et al. MFFNet: multi-dimensional feature fusion network based on attention mechanism for sEMG analysis to detect muscle fatigue[J]. Expert Systems with Applications, 2021, 185: 115639. [27] Navaneethakrishna M, Ramakrishnan S. Multiscale feature based analysis of surface EMG signals under fatigue and non-fatigue conditions[C]//2014 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE, 2014: 4627-4630. [28] Hussain I, Salvietti G, Spagnoletti G, et al. The soft-sixthfinger: a wearable EMG controlled robotic extra-finger for grasp compensation in chronic stroke patients[J]. IEEE Robotics and Automation Letters, 2016, 1(2): 1000-1006. [29] Tacca N, Dunlap C, Donegan S P, et al. Wearable high-density EMG sleeve for complex hand gesture classification and continuous joint angle estimation[J]. Scientific Reports, 2024, 14(1): 18564. [30] Chen Peiji, Li Ziye, Togo Shunta, et al. A layered sEMG-FMG hybrid sensor for hand motion recognition from forearm muscle activities[J]. IEEE Transactions on Human-Machine Systems, 2023, 53(5): 935-944. [31] Sun Zhe, Huang Zihao, Duan Feng, et al. A novel multimodal approach for hybrid brain-computer interface[J]. IEEE Access, 2020, 8: 89909-89918. [32] Kristeva R, Popa T, Chakarov V, et al. Cortico-muscular coupling in a patient with postural myoclonus[J]. Neuroscience letters, 2004, 366(3): 259-263. [33] Ushiyama J, Takahashi Y, Ushiba J. Muscle dependency of corticomuscular coherence in upper and lower limb muscles and training-related alterations in ballet dancers and weightlifters[J]. Journal of Applied Physiology, 2010, 109(4): 1086-1095. [34] Witham CL, Riddle CN, Baker MR, et al. Contributions of descending and ascending pathways to corticomuscular coherence in humans[J]. The Journal of Physiology, 2011, 589(15): 3789-3800. [35] 谢平, 陈迎亚, 张园园, 等. 基于 Gabor 小波和格兰杰因果的脑-肌电同步性分析[J]. 中国生物医学工程学报, 2017, 36(1): 28-38. [36] Xi Xugang, Ma Cunbin, Yuan Changmin, et al. Enhanced EEG-EMG coherence analysis based on hand movements[J]. Biomedical Signal Processing and Control, 2020, 56: 101727. [37] Xi Xugang, Sun Ziyang, Hua Xian, et al. Construction and analysis of cortical-muscular functional network based on EEG-EMG coherence using wavelet coherence[J]. Neurocomputing, 2021, 438: 248-258. [38] Sun Jingyao, Jia Tianyu, Li Zhibin, et al. Enhancement of EEG-EMG coupling detection using corticomuscular coherence with spatial-temporal optimization[J]. Journal of Neural Engineering, 2023, 20(3): 036001. [39] Ohara S, Nagamine T, Ikeda A, et al. Electrocorticogram-electromyogram coherence during isometric contraction of hand muscle in human[J]. Clinical Neurophysiology, 2000, 111(11): 2014-2024. [40] 李素姣, 苏刘, 贺蓝, 等. 脑肌电信号同步耦合分析方法研究进展[J]. 生物医学工程学杂志, 2019, 36(2): 334-337. [41] 谢平, 杨芳梅, 李欣欣, 等. 基于变分模态分解-传递熵的脑肌电信号耦合分析[J]. 物理学报, 2016, 65(11): 118701. [42] Guo Zhenghao, McClelland VM, Simeone O, et al. Multiscale wavelet transfer entropy with application to corticomuscular coupling analysis[J]. IEEE Transactions on Biomedical Engineering, 2021, 69(2): 771-782. [43] 陈迎亚. 康复运动中脑肌电融合及同步分析方法研究[D]. 秦皇岛: 燕山大学, 2016. [44] Chowdhury A, Raza H, Meena YK, et al. An EEG-EMG correlation-based brain-computer interface for hand orthosis supported neuro-rehabilitation[J]. Journal of Neuroscience Methods, 2019, 312: 1-11. [45] Li Sheng, Francisco GE, Rymer WZ. A new definition of poststroke spasticity and the interference of spasticity with motor recovery from acute to chronic stages[J]. Neurorehabilitation and Neural Repair, 2021, 35(7): 601-610. [46] Ferrante S, Pedrocchi A, Ferrigno G, et al. Cycling induced by functional electrical stimulation improves the muscular strength and the motor control of individuals with post-acute stroke[J]. Eur J Phys Rehabil Med, 2008, 44(2): 159-167. [47] 姜亚斌, 邹任玲, 刘建. 表面肌电信号的肌肉疲劳判别研究进展[J]. 生物信息学, 2017, 15(2):120-126. [48] Hunter SK, Pereira HM, Keenan KG. The aging neuromuscular system and motor performance[J]. Journal of Applied Physiology, 2016, 121(4): 982-995. [49] Nelles G, Spiekermann G, Jueptner M, et al. Reorganization of sensory and motor systems in hemiplegic stroke patients: a positron emission tomography study[J]. Stroke, 1999, 30(8): 1510-1516. [50] Ferrante S, Pedrocchi A, Ferrigno G, et al. Cycling induced by functional electrical stimulation improves the muscular strength and the motor control of individuals with post-acute stroke[J]. Eur J Phys Rehabil Med, 2008, 44(2): 159-167. [51] Ambrosini E, Ferrante S, Schauer T, et al. A myocontrolled neuroprosthesis integrated with a passive exoskeleton to support upper limb activities[J]. Journal of Electromyography and Kinesiology, 2014, 24(2): 307-317. [52] Ambrosini E, Parati M, Peri E, et al. Changes in leg cycling muscle synergies after training augmented by functional electrical stimulation in subacute stroke survivors: a pilot study[J]. Journal of Neuroengineering and Rehabilitation, 2020, 17(1): 1-14. [53] Shin HE, Kim M, Lee D, et al. Therapeutic effects of functional electrical stimulation on physical performance and muscle strength in post-stroke older adults: a review[J]. Annals of Geriatric Medicine and Research, 2022, 26(1): 16-24. [54] Dhyani P, Goyal C, Dhull SB, et al. Psychobiotics for mitigation of neuro-degenerative diseases: recent advancements[J]. Molecular Nutrition & Food Research, 2024, 68(13): 2300461. [55] Khasawneh MA. Accessibility matters: investigating the usability of social media platforms for individuals with motor disabilities[J]. Studies in Media and Communication, 2024, 12(2): 1-11. [56] Wang Meng, Lin Yi, Gu Feiyu, et al. Diagnosis of cognitive and motor disorders levels in stroke patients through explainable machine learning based on MRI[J]. Medical Physics, 2024, 51(3): 1763-1774. [57] Katsura Y, Ohga S, Shimo K, et al. A decision tree algorithm to identify predictors of post-stroke complex regional pain syndrome[J]. Scientific Reports, 2024, 14(1): 9893. [58] Sengupta N, Rao A S, Yan B, et al. A survey of wearable sensors and machine learning algorithms for automated stroke rehabilitation[J]. IEEE Access, 2024. 36026-36054. [59] 许敏鹏, 罗睿心, 韩锦, 等. 刺激视野面积对稳态视觉诱发电位的影响[J]. 信号处理, 2022, 38(10): 2064-2073. [60] 许敏鹏, 吴乔逸, 熊文田, 等. 面向中高频 SSVEP 脑机接口的编解码算法研究[J]. 信号处理, 2022, 38(9): 1881-1891.
