伴随规则节律的运动想象方案设计

doi:10.3969/j.issn.1006-9771.2019.05.018

Abstract

Abstract: Objective To propose a motor imagery paradigm with regular rhythm to improve the accuracy of classification in brain-computer 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.

Key words: motor imagery, brain-computer interface, active rehabilitation, rehabilitation robots

CLC Number:

R496

WU Hong-jian, LI Long, GUO Jing, LI Li-na, LIU Tian, WANG Jue. Program Design of Motor Imagery with Regular Rhythm[J]. 《Chinese Journal of Rehabilitation Theory and Practice》, 2019, 25(5): 593-596.

References

1 DobkinB H. Clinical practice. Rehabilitation after stroke [J]. New Engl J Med, 2005, 352(16): 1677-1684.
2 HyeonU K N, SeokH J, NaY 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 FrolovA A, MokienkoO, LyukmanovR, 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 PichiorriF, MoroneG, PettiM, et al. Brain-computer interface boosts motor imagery practice during stroke recovery [J]. Ann Neurol, 2015, 77(5): 851-865.
8 AngK K, GuanC, PhuaK S, et al. Brain-computer interface-based 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 AnderR M, DorisB, MassimilianoR, 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 ObermanL M, HubbardE M, McCleeryJ 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 CattaneoL, RizzolattiG. The mirror neuron system [J]. Annu Rev Neurosci, 2009, 27(5): 557-560.
13 LiL, WangJ, XuG, et al. The study of object-oriented motor imagery based on EEG suppression [J]. PLoS One, 2015, 10(12): e0144256-0144263.
14 TangZ, SunS, ZhangS, et al. A brain-machine interface based on ERD/ERS for an upper-limb exoskeleton control [J]. Sensors, 2016, 16(12): 2050-2057.
15 SamieeK, KovácsP, GabboujM. 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 PopescuF, FazliS, BadowerY, et al. Single trial classification of motor imagination using 6 dry EEG electrodes [J]. PLoS One, 2007, 2(7): e637-644.
17 BlankertzB, TomiokaR, LemmS, et al. Optimizing spatial filters for robust EEG single-trial analysis [J]. IEEE Signal Proc Mag, 2007, 25(1): 41-56.
18 PfurtschellerG, NeuperC, GugerC, et al. Current trends in Graz Brain-Computer Interface (BCI) research [J]. IEEE Trans Rehabil Eng, 2000, 8(2): 216-219.
19 LemmS, BlankertzB, CurioG, et al. Spatio-spectral filters for improving the classification of single trial EEG [J]. IEEE T Biomed Eng, 2005, 52(9): 1541-1548.
20 OzmenN G, GumuselL. Mental and Motor Task Classification by LDA [M]. Berlin, Heidelberg: Springer, 2010.
21 SchererR, FallerJ, FriedrichE 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 HasanM R, IbrahimyM I, MotakabberS 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-RosaJ J, NataliF, TettamantiA, 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 FokS, SchwartzR, WronkiewiczM, 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 ComaniS, VellutoL, SchinaiaL, 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 AngK K, ChuaK S, PhuaK 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.

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Program Design of Motor Imagery with Regular Rhythm

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