张凯,徐光华,李文平,李辉,杜成航,韩丞丞,张四聪,谢杰仁

• 1:西安交通大学机械工程学院
• 2:西安交通大学机械制造系统工程国家重点实验室

摘要(Abstract)：

为解决运动诱发脑-机交互过程中，意念感知环路与运动反馈神经环路协同激活时序同步性弱的问题，受到脉冲时间依赖的突触可塑性理论的启发，开展了运动激活响应的时序作用规律探索与闭环脑机时序同步强化的方法研究。基于应激刺激下运动中枢激活响应特性，通过采集与整合不同应激运动响应任务下力-位移-脑电多源信息，开展了应激诱发下中枢环路时序激活响应检测方案设计；基于检测得到的时序参数，应用脑功能网络分析技术对脑-机交互过程运动感知反馈激活时序规律进行了有效量化。通过在线实验验证和统计分析，验证了应激运动诱发范式保留了传统运动诱发模型的特点：既能够不通过真实运动而激活皮层运动区域，又能有效增强受试者在脑控闭环过程中的运动感知激活响应时效性；在受试者群体中，闭环脑控的感知与反馈响应的时延在0.15～0.90 s之间，最大激活响应时间的持续时间为400～600 ms,激活响应的同步性相较于传统范式具有增强效果。所提方法揭示了应激诱发脑机调控过程的时序作用规律，可为实现运动感知同步性增强的脑-机接口设计提供有力的理论支撑。

关键词(KeyWords)： 脑-机接口;应激诱发;运动感知与反馈;时序规律;同步

Abstract:

Keywords:

基金项目(Foundation): 科技创新2030重大项目（2021ZD0204300）;; 陕西省重点研发计划资助项目（2021GXLH-Z-008）

作者(Author): 张凯,徐光华,李文平,李辉,杜成航,韩丞丞,张四聪,谢杰仁

参考文献(References)：

• [1] YUAN Han,HE Bin.Brain-computer interfaces using sensorimotor rhythms:current state and future perspectives [J].IEEE Transactions on Biomedical Engineering,2014,61(5):1425-1435.
• [2] DONATI A R C,SHOKUR S,MORYA E,et al.Long-term training with a brain-machine interface-based gait protocol induces partial neurological recovery in paraplegic patients [J].Scientific Reports,2016,6:30383.
• [3] PARK E C,HWANGBO G.The effects of action observation gait training on the static balance and walking ability of stroke patients [J].Journal of Physical Therapy Science,2015,27(2):341-344.
• [4] BUNDY D T,SOUDERS L,BARANYAI K,et al.Contralesional brain-computer interface control of a powered exoskeleton for motor recovery in chronic stroke survivors [J].Stroke,2017,48(7):1908-1915.
• [5] RUBIN D B,AJIBOYE A B,BAREFOOT L,et al.Interim safety profile from the feasibility study of the BrainGate neural interface system [J].Neurology,2023,100(11):1177-1192.
• [6] MORONE G,PICHIORRI F.Post-stroke rehabilitation:challenges and new perspectives [J].Journal of Clinical Medicine,2023,12(2):550.
• [7] 余敏，周一心，陆静珏，等.神经康复学科研究进展 [J].上海医药，2015,36(22):4-8.YU Min,ZHOU Yixin,LU Jingjue,et al.Research progress in neurorehabilitation [J].Shanghai Medical & Pharmaceutical Journal,2015,36(22):4-8.
• [8] DING Yuchuan,LI Jie,CLARK J,et al.Synaptic plasticity in thalamic nuclei enhanced by motor skill training in rat with transient middle cerebral artery occlusion [J].Neurological Research,2003,25(2):189-194.
• [9] LUQUE N R,GARRIDO J A,NAVEROS F,et al.Distributed cerebellar motor learning:a spike-timing-dependent plasticity model [J].Frontiers in Computational Neuroscience,2016,10:17.
• [10] BONASSI G,BIGGIO M,BISIO A,et al.Provision of somatosensory inputs during motor imagery enhances learning-induced plasticity in human motor cortex [J].Scientific Reports,2017,7(1):9300.
• [11] MALOUIN F,RICHARDS C L,JACKSON P L,et al.The kinesthetic and visual imagery questionnaire (KVIQ) for assessing motor imagery in persons with physical disabilities:a reliability and construct validity study [J].Journal of Neurologic Physical Therapy,2007,31(1):20-29.
• [12] ZHANG Chenguang,JU Fen,SUN Wei,et al.Effects of training with a brain-computer interface-controlled robot on rehabilitation outcome in patients with subacute stroke:a randomized controlled trial [J].Neurology and Therapy,2022,11(2):679-695.
• [13] 《运动解剖学、运动医学大辞典》编辑委员会.运动解剖学、运动医学大辞典 [M].北京：人民体育出版社，2000.
• [14] FADEEV K A,SMIRNOV A S,ZHIGALOVA O P,et al.Too real to be virtual:autonomic and EEG responses to extreme stress scenarios in virtual reality [J].Behavioural Neurology,2020,2020:5758038.
• [15] ZHANG Kai,XU Guanghua,DU Chenghang,et al.Enhancement of capability for motor imagery using vestibular imbalance stimulation during brain computer interface [J].Journal of Neural Engineering,2021,18(5):056064.
• [16] COITO A,MICHEL C M,VULLIEMOZ S,et al.Directed functional connections underlying spontaneous brain activity [J].Human Brain Mapping,2019,40(3):879-888.
• [17] 梁夏，王金辉，贺永.人脑连接组研究：脑结构网络和脑功能网络 [J].科学通报，2010,55(16):1565-1583.LIANG Xia,WANG Jinhui,HE Yong.Human connectome:structural and functional brain networks [J].Chinese Science Bulletin,2010,55(16):1565-1583.
• [18] HOODGAR M,KHOSROWABADI R,NAVI K,et al.Brain functional connectivity changes during learning of time discrimination [J].Basic and Clinical Neuroscience,2022,13(4):531-550.
• [19] LU C F,TENG S,HUNG C I,et al.Reorganization of functional connectivity during the motor task using EEG time-frequency cross mutual information analysis [J].Clinical Neurophysiology,2011,122(8):1569-1579.
• [20] REGGIANI E,D’ARNESE E,PURGATO A,et al.Pearson correlation coefficient acceleration for modeling and mapping of neural interconnections [C]//2017 IEEE International Parallel and Distributed Processing Symposium Workshops (IPDPSW).Piscataway,NJ,USA:IEEE,2017:223-228.
• [21] LIU Jianping,ZHANG Chong,ZHENG Chongxun.Estimation of the cortical functional connectivity by directed transfer function during mental fatigue [J].Applied Ergonomics,2010,42(1):114-121.
• [22] KHAN D M,YAHYA N,KAMEL N,et al.A novel method for efficient estimation of brain effective connectivity in EEG [J].Computer Methods and Programs in Biomedicine,2023,228:107242.
• [23] RUBINOV M,SPORNS O.Complex network measures of brain connectivity:uses and interpretations [J].NeuroImage,2010,52(3):1059-1069.
• [24] LI Fali,LIU Tiejun,WANG Fei,et al.Relationships between the resting-state network and the P3:evidence from a scalp EEG study [J].Scientific Reports,2015,5(1):15129.
• [25] TOPPI J,PETTI M,MATTIA D,et al.Time-varying effective connectivity for investigating the neurophysiological basis of cognitive processes [M]//SAKKALIS V.Modern Electroencephalographic Assessment Techniques:Theory and Applications.New York,NY:Springer New York,2015:171-204.
• [26] MANSOORY M S,ALLAHVERDY A,BEHBOUDI M,et al.Local efficiency analysis of resting state functional brain network in methamphetamine users [J].Behavioural Brain Research,2022,434:114022.