DOI: 10.3724/SP.J.1218.2011.00307

Robot (机器人) 2011/33:3 PP.307-313

Upper-limb Rehabilitation Robot Based on Motor Imagery EEG

For the rehabilitation training of hemiplegia patients caused by stroke, an upper-limb rehabilitation robot system based on motor imagery electroencephalography (EEG) is designed. Firstly, three-dimensional animation is used to stimulate the patient to imagine the upper-limb movement, and EEG signal is acquired by EEG amplifier via universal serial bus (USB). Secondly, eigenvector is extracted by wavelet packet transform (WPT), and linear discriminant analysis (LDA) classifier based on the Mahalanobis distance is utilized to classify the pattern. Finally, PC gives the visual feedback information based on virtual reality and controls the rehabilitation robot. The patient’s upper-limb motor imagery EEG is used to control rehabilitation robot directly and it can accelerate the recovery of motor nerve function. Six subjects have been tested for a long time using this system. The results show the feasibility of the whole system.

Key words:rehabilitation robot,virtual reality,motor imagery,neurological rehabilitation

ReleaseDate:2014-07-21 15:51:17

[1] Lum P S, Burgar C G, Shor P C, et al. Robot-assisted movement training compared with conventional therapy techniques for the rehabilitation of upper-limb motor function after stroke[J]. Archives of Physical Medicine and Rehabilitation, 2002, 83(7): 952-959.

[2] Riener R, Nef T, Colombo G. Robot-aided neurorehabilitation of the upper extremities[J]. Medical & Biological Engineering & Computing, 2005, 43(1): 2-10.

[3] Dario P, Guglielmelli E, Genovese V, et al. Robot assistants: Applications and evolution[J]. Robotics and Autonomous Systems, 1996, 18(1/2): 225-234.

[4] Topping M. An overview of the development of Handy 1, a rehabilitation robot to assist the severely disabled[J]. Journal of Intelligent and Robotic Systems, 2002, 34(3): 253-263.

[5] Krebs H I, Hogan N, Aisen M L, et al. Robot-aided neurorehabilitation[J]. IEEE Transactions on Rehabilitation Engineering, 1998, 6(1): 75-87.

[6] Loureiro R, Amirabdollahian F, Topping M, et al. Upper limb robot mediated stroke Therapy–GENTLE/s approach[J]. Autonomous Robots, 2003, 15(1): 35-51.

[7] Reinkensmeyer D J, Kahn L E, Averbuch M, et al. Understanding and teaching arm movement impairment after chronic brain injury: Progress with the arm guide[J]. Journal of Rehabilitation Research and Development, 2000, 37(6): 653-662.

[8] 李会军,宋爱国.上肢康复训练机器人虚拟环境建模技术[J].中国组织工程研究与临床康复,2007,11(44): 8877-8881. Li H J, Song A G. Virtual environment building for a rehabilitative robot of the upper-limb[J]. Journal of Clinical Rehabilita tive Tissue Engineering Research, 2007, 11(44): 8877-8881.

[9] Carr J H, Shepherd R B. A motor relearning programme for stroke[M]. 2nd ed. New York, NY, USA: Aspen Publisher, 1987: 151-154.

[10] Daly J J, Wolpaw J R. Brain-computer interfaces in neurological rehabilitation[J]. The Lancet Neurology, 2008, 7(11): 1032-1043.

[11] 徐宝国,何乐生,宋爱国.基于脑电信号的人机交互实验平台的设计和应用[J].电子测量与仪器,2008,22(1): 81-85.Xu B G, He L S, Song A G. Design and application of human-machine interactive experimental platform based on EEG[J]. Journal of Electronic Measurement and Instrument, 2008, 22(1): 81-85.

[12] Pfurtscheller G, Lopes da Silva F H. Event-related EEG/MEG synchronization and desynchronization: Basic principles[J]. Clinical Neurophysiology, 1999, 110(11): 1842-1857.

[13] Houdayer E, Labyt E, Cassim F, et al. Relationship between event-related beta synchronization and afferent inputs: Analysis of finger movement and peripheral nerve stimulations[J]. Clinical Neurophysiology, 2006, 117(3): 628-636.

[14] Rosso O A, Martin M T, Figliola A, et al. EEG analysis using wavelet-based information tools[J]. Journal of Neuroscience Methods, 2006, 153(2): 163-182.

[15] Lotte F, Congedo M, Lecuyer A, et al. A review of classification algorithms for EEG-based brain-computer interfaces[J]. Journal of Neural Engineering, 2007, 4(2): R1-R13.

[16] Besserve M, Jerbi K, Laurent F, et al. Classification methods for ongoing EEG and MEG signals[J]. Biological Research, 2007, 40(4): 415-437.