运动相关思维诱发脑电信息解码与应用综述
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摘要
运动是人类日常思维与活动最基本、最重要的必需功能之一,各式动作通过神经系统调节肌肉收缩或舒张得以实现。研究运动相关思维诱发大脑神经生理活动信息不仅可深入揭示脑认知与行为的内在神经原理和调控机制,还能为研究开发新型脑-机接口(BCI)系统、更有效辅助运动障碍患者功能康复提供关键科学依据与创新设计思路,具有显见的学术意义和应用价值。主要综述了运动想象(MI)与运动执行(ME)思维所诱发不同脑电(EEG)神经生理特征的异同;重点回顾了基于运动相关思维EEG信息解码BCI在运动意图检测、特定局部肢体运动分类及参数解码与应用的最新研究进展;分析了阻碍其发展的技术难点并探讨了可能化解思路及展望了其未来前景;以期促进相关BCI技术的深入研究与开发应用。
Motor is one of the most basic and important functions of human beings for daily life. Various forms of movements are achieved by the muscle contraction or relaxation regulated by nervous system. Studying brain neurophysiological information induced by motor-related mental activity can not only reveal the intrinsic neurological principles and mechanisms of brain cognition and behavior, but also provide key scientific basis and innovative design ideas for the novel brain-computer interface(BCI) systems and the more effective rehabilitation of patients with motor disorders. Therefore, it has obvious academic significance and application value. This paper mainly summarizes the similarities and differences of neurophysiological features of different electroencephalography(EEG) induced by motor imagery(MI) and motor execution(ME). The latest research progress of motor-related information decoding is focused based BCI(detection of movement intention, classification of specific localized limb movement, and movement parameters decoding). The technical challenges and the possible solutions are discussed, and the further prospects are expected in order to promote the development and application of relevant BCI technology.
Motor is one of the most basic and important functions of human beings for daily life. Various forms of movements are achieved by the muscle contraction or relaxation regulated by nervous system. Studying brain neurophysiological information induced by motor-related mental activity can not only reveal the intrinsic neurological principles and mechanisms of brain cognition and behavior, but also provide key scientific basis and innovative design ideas for the novel brain-computer interface(BCI) systems and the more effective rehabilitation of patients with motor disorders. Therefore, it has obvious academic significance and application value. This paper mainly summarizes the similarities and differences of neurophysiological features of different electroencephalography(EEG) induced by motor imagery(MI) and motor execution(ME). The latest research progress of motor-related information decoding is focused based BCI(detection of movement intention, classification of specific localized limb movement, and movement parameters decoding). The technical challenges and the possible solutions are discussed, and the further prospects are expected in order to promote the development and application of relevant BCI technology.
引文
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[12] VILLIGER M, HEPPREYMOND M C, KIPER D, et al. Enhanced activation of motor execution networks using action observation combined with imagination of lower limb movements[J]. Plos One, 2013(8):e72403.
[13] 明东, 王坤, 何峰, 等. 想象动作诱发生理信息检测及其应用研究:回顾与展望[J]. 仪器仪表学报, 2014, 35(9):1921- 1931.MING D, WANG K, HE F, et al. Study on physiological information detection and application evoked by motor imagery: Review and prospect[J]. Chinese Journal of Scientific Instrument, 2014, 35(9):1921- 1931.
[14] JAHANSHAHI M, HALLETT M. The Bereitschaftspotential[M]. New York: Springer US, 2003.
[15] DO NASCIMENTO O F,NIELSEN K D,VOIGT M. Movement-related parameters modulate cortical activity during imaginary isometric plantar-flexions[J]. Experimental Brain Research, 2006, 171(1):78- 90.
[16] SHIBASAKI H, HALLETT M. What is the Bereitschaftspotential? [J]. Clinical Neurophysiology, 2006, 117(11):2341- 2356.
[17] SMULDERS F T Y, MILLER J O. The Oxford Handbook of Event-Related Potential Components[M]. Oxford: Oxford University Press, 2012: 209- 229.
[18] DI R F, PITZALIS S, APRILE T, et al. Effect of practice on brain activity: An investigation in top-level rifle shooters[J]. Medicine & Science in Sports & Exercise, 2005, 37(9):1586- 1593.
[19] NIAZI I K, JIANG N, TIBERGHIEN O, et al. Detection of movement intention from single-trial movement-related cortical potentials[J]. Journal of Neural Engineering, 2011, 8(6):66009.
[20] JOCHUMSEN M, NIAZI I K, TAYLOR D, et al. Detecting and classifying movement-related cortical potentials associated with hand movements in healthy subjects and stroke patients from single-electrode, single-trial EEG[J]. Journal of Neural Engineering, 2015, 12(5):56013.
[21] 奕伟波. 基于复合肢体想象动作的脑电特征识别技术研究[D]. 天津:天津大学, 2012.YI W B. Research on EEG feature recognition technique based on compound-limbs motor imagery[D]. Tianjin: Tianjin University, 2012.
[22] PFURTSCHELLER G, BRUNNER C, SCHL?GL A, et al. Mu rhythm (de)synchronization and EEG single-trial classification of different motor imagery tasks[J]. Neuroimage, 2006, 31(1):153.
[23] PFURTSCHELLER G, LOPES D S F H. Event-related EEG/MEG synchronization and desynchronization: Basic principles [J]. Clinical Neurophysiology,1999, 110(11): 1842- 1857.
[24] ZHUANG P, TORO C, GRAFMAN J, et al. Event-related desynchronization (ERD) in the alpha frequency during development of implicit and explicit learning[J]. Electroencephalography & Clinical Neurophysiology, 1997, 102(4):374- 381.
[25] ZhOU Z X, WAN B K, MING D, et al. A novel technique for phase synchrony measurement from the complex motor imaginary potential of combined body and limb action[J]. Journal of Neural Engineering, 2010, 7(4):46008.
[26] YI W, QIU S, WANG K, et al. EEG oscillatory patterns and classification of sequential compound limb motor imagery[J]. Journal of Neuroengineering & Rehabilitation, 2016, 13(1):1- 12.
[27] NAGAMINE T, KAJOLA M, SALMELIN R, et al. Movement-related slow cortical magnetic fields and changes of spontaneous MEG-and EEG-brain rhythms[J]. Electroencephalography & Clinical Neurophysiology, 1996, 99(3):274- 286.
[28] SOLODKIN A, HLUSTIK P, CHEN E E, et al. Fine modulation in network activation during motor execution and motor imagery[J]. Cerebral Cortex, 2004, 14(11):1246- 1255.
[29] FRAK V, PAULIGNAN Y, JEANNEROD M. Orientation of the opposition axis in mentally simulated grasping[J]. Experimental Brain Research, 2001, 136(1):120- 127.
[30] JEANNEROD M. The representing brain: Neural correlates of motor intention and imagery[J]. Behavioral & Brain Sciences, 1994, 17(2):187- 202.
[31] DECETY J, JEANNEROD M, PRABLANC C. The timing of mentally represented actions[J]. Behavioural Brain Research, 1989, 34(1):35- 42.
[32] CERRITELLI B, MARUFF P, WILSON P, et al. The effect of an external load on the force and timing components of mentally represented actions[J]. Behavioural Brain Research, 2000, 108(1):91- 96.
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