伴随规则节律的运动想象方案设计
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摘要
目的提出一种伴随规则节律的运动想象范式,以解决脑机接口运动想象康复机器人分类准确率偏低的问题。方法未经运动想象训练的健康被试10例,通过动作图片提示进行运动想象引导,图片内容为伴随规则节律的运动动作。利用共同空间模式特征提取算法和Fisher分类器进行分类,与传统范式和伴随无规则节律的运动想象范式进行对比。结果伴规则节律的运动想象范式出现更加明显的去同步现象,分类准确率明显提高。结论伴随规则节律的运动想象方案为基于脑机接口的主动康复训练研究提供新思路。
Objective To propose a motor imagery paradigm with regular rhythm to improve the accuracy of classification in braincomputer interface based on motor imagery.Methods Ten untrained healthy subjects were asked to image the movement guided by the picture accompanied with regular rhythm. The common spatial pattern feature extraction algorithm and Fisher classifier were used for classification, and compared with the traditional paradigm and the motor imagery paradigm accompanying with irregular rhythm.Results More desynchronization was found as motor imagery with regular rhythm, while the classification accuracy improved.Conclusion Motor imagery with regular rhythm gives a new idea for active rehabilitation training based on brain-computer interface.
Objective To propose a motor imagery paradigm with regular rhythm to improve the accuracy of classification in braincomputer interface based on motor imagery.Methods Ten untrained healthy subjects were asked to image the movement guided by the picture accompanied with regular rhythm. The common spatial pattern feature extraction algorithm and Fisher classifier were used for classification, and compared with the traditional paradigm and the motor imagery paradigm accompanying with irregular rhythm.Results More desynchronization was found as motor imagery with regular rhythm, while the classification accuracy improved.Conclusion Motor imagery with regular rhythm gives a new idea for active rehabilitation training based on brain-computer interface.
引文
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[25]Comani S,Velluto L,Schinaia L,et al.Monitoring neuro-motor recovery from stroke with high-resolution EEG,robotics and virtual reality:a proof of concept[J].IEEE Trans Neural Syst Rehabil Eng,2015,23(6):1106-1116.
[26]Ang K K,Chua K S,Phua K S,et al.A randomized controlled trial of EEG-based motor imagery brain-computer interface robotic rehabilitation for stroke[J].Clin EEG Neurosci,2014,46(4):310-317.
[2]Hyeon U K N,Seok H J,Na Y J,et al.Effect of dominant hand paralysis on quality of life in patients with subacute stroke[J].Ann Rehabil Med,2014,38(4):450-457.
[3]郭晓辉,王晶,徐光华.手部功能康复机器人研究最新进展[J].中国康复医学杂志,2017,32(2):235-240.
[4]李芳,郑洁皎.手康复机器人治疗脑卒中后手功能障碍的研究进展[J].中华物理医学与康复杂志,2016,38(9):709-712.
[5]李海丽,谢叻,魏盛凯,等.手功能康复机器人技术[J].机械设计与研究,2014,30(1):24-28.
[6]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,2017,2017(11):400-410.
[7]Pichiorri F,Morone G,Petti M,et al.Brain-computer interface boosts motor imagery practice during stroke recovery[J].Ann Neurol,2015,77(5):851-865.
[8]Ang K K,Guan C,Phua K S,et al.Brain-computer interfacebased robotic end effector system for wrist and hand rehabilitation:results of a three-armed randomized controlled trial for chronic stroke[J].Front Neuroeng,2014,7(30):1-8.
[9]Ander R M,Doris B,Massimiliano R,et al.Brain-machine interface in chronic stroke rehabilitation:a controlled study[J].Ann Neurol,2013,74(1):100-108.
[10]张桃,杨帮华,段凯文,等.基于运动想象脑机接口的手功能康复系统设计[J].中国康复理论与实践,2017,23(1):4-9.
[11]Oberman L M,Hubbard E M,McCleery J P,et al.EEG evidence for mirror neuron dysfunction in autism spectrum disorders[J].Brain Res Cogn Brain Res,2005,24(2):190-198.
[12]Cattaneo L,Rizzolatti G.The mirror neuron system[J].Annu Rev Neurosci,2009,27(5):557-560.
[13]Li L,Wang J,Xu G,et al.The study of object-oriented motor imagery based on EEG suppression[J].PLoS One,2015,10(12):e0144256-0144263.
[14]Tang Z,Sun S,Zhang S,et al.A brain-machine interface based on ERD/ERS for an upper-limb exoskeleton control[J].Sensors,2016,16(12):2050-2057.
[15]Samiee K,Kovács P,Gabbouj M.Epileptic seizure classification of EEG time-series using rational discrete short-time Fourier transform[J].IEEE T BIO-MED ENG,2015,62(2):541-552.
[16]Popescu F,Fazli S,Badower Y,et al.Single trial classification of motor imagination using 6 dry EEG electrodes[J].PLoS One,2007,2(7):e637-644.
[17]Blankertz B,Tomioka R,Lemm S,et al.Optimizing spatial filters for robust EEG single-trial analysis[J].IEEE Signal Proc Mag,2007,25(1):41-56.
[18]Pfurtscheller G,Neuper C,Guger C,et al.Current trends in Graz Brain-Computer Interface(BCI)research[J].IEEE Trans Rehabil Eng,2000,8(2):216-219.
[19]Lemm S,Blankertz B,Curio G,et al.Spatio-spectral filters for improving the classification of single trial EEG[J].IEEE TBiomed Eng,2005,52(9):1541-1548.
[20]Ozmen N G,Gumusel L.Mental and Motor Task Classification by LDA[M].Berlin,Heidelberg:Springer,2010.
[21]Scherer R,Faller J,Friedrich E V,et al.Individually adapted imagery improves brain-computer interface performance in end-users with disability[J].PLoS One,2015,10(5):e0123727-0123734.
[22]Hasan M R,Ibrahimy M I,Motakabber S M A,et al.Classification of multichannel EEG signal by linear discriminant analysis[J].Adv Inte Sys Com,2015,2015(1089):279-282.
[23]Gonzalez-Rosa J J,Natali F,Tettamanti A,et al.Action observation and motor imagery in performance of complex movements evidence from EEG and kinematics analysis[J].Behav Brain Res,2015,2015(281):290-300.
[24]Fok S,Schwartz R,Wronkiewicz M,et al.An EEG-based brain-computer interface for rehabilitation and restoration of hand control following stroke using ipsilateral cortical physiology[M]//IEEE Engineering in Medicine and Biology Society Conference Proceedings.Boston,MA:IEEE,2011:6277-6280.
[25]Comani S,Velluto L,Schinaia L,et al.Monitoring neuro-motor recovery from stroke with high-resolution EEG,robotics and virtual reality:a proof of concept[J].IEEE Trans Neural Syst Rehabil Eng,2015,23(6):1106-1116.
[26]Ang K K,Chua K S,Phua K S,et al.A randomized controlled trial of EEG-based motor imagery brain-computer interface robotic rehabilitation for stroke[J].Clin EEG Neurosci,2014,46(4):310-317.