Abstract:Brain-computer interface (BCI) based on motor imagery (MI) is a new rehabilitation method, which plays a significant role in helping to improve and restore physical functions of patients. However, MI-BCI still faces many challenges for practical application, including the low spatial resolution of physiological signals induced by MI, the long training time of users, and the difficulty in implementing an asynchronous control MI-BCI system. This paper briefly outlined the research of MI-related mechanisms, reviewed the relevant solutions and the research status from the aspects of signal acquisition, signal processing algorithm analysis, paradigm design and asynchronous control research. At last, we outlined the application and perspectives of MI-BCI in the future development.
作者简介: #中国生物医学工程学会会员(Member, Chinese Society of Biomedical Engineering)
引用本文:
赵欣, 陈志堂, 王坤, 王仲朋, 周鹏, 綦宏志. 运动想象脑-机接口新进展与发展趋势[J]. 中国生物医学工程学报, 2019, 38(1): 84-93.
Zhao Xin, Chen Zhitang, Wang Kun, Wang Zhongpeng, Zhou Peng, Qi Hongzhi. New Developments and Trends of BCI Based on Motor Imagery. Chinese Journal of Biomedical Engineering, 2019, 38(1): 84-93.
[1] Clerc M. Brain computerinterfaces, Principles and practise [J]. Biomedical Engineering Online, 2013, 12(1): 1-4.
[2] Scherer R, Müller GR, Neuper C, et al. An asynchronously controlled EEG-based virtual keyboard: improvement of the spelling rate [J]. IEEE Trans Biomed Eng, 2004, 51(6): 979-984.
[3] Wolpaw JR, Mcfarland DJ, Neat GW, et al. An EEG-based brain-computer interface for cursor control [J]. Electroencephalogr Clin Neurophysiol, 1991, 78(3): 252-259.
[4] Carlson T, Millan JDR. Brain-controlled wheelchairs: A robotic architecture [J]. IEEE Robotics & Automation Magazine, 2013, 20(1): 65-73.
[5] Meng J, Zhang S, Bekyo A, et al. Noninvasive electroencephalogram based control of a robotic arm for reach and grasp tasks [J]. Sci Rep, 2016, 6:38565.
[6] Grossewentrup M, Mattia D, Oweiss K. Using brain-computer interfaces to induce neural plasticity and restore function [J]. J Neural Eng, 2011, 8(2): 025004.
[7] Caria A, Weber C, Brötz D, et al. Chronic stroke recovery after combined BCI training and physiotherapy: A case report [J]. Psychophysiology, 2011, 48(4): 578-582.
[8] Remsik A, Young B, Vermilyea R, et al. A review of the progression and future implications of brain-computer interface therapies for restoration of distal upper extremity motor function after stroke [J]. Expert Rev Med Devices, 2016, 13(5): 445-454.
[9] 李明芬, 贾杰, 刘烨, 等. 基于运动想象的脑机接口康复训练对脑卒中患者上肢运动功能的影响 [J]. 老年医学与保健, 2012, 18(6): 347-352.
[10] 刘小燮, 毕胜, 高小榕, 等. 基于运动想象的脑机交互康复训练新技术对脑卒中大脑可塑性影响 [J]. 中国康复医学杂志, 2013, 28(2): 97-102.
[11] Szameitat AJ, Shen S, Sterr A. Motor imagery of complex everyday movements. An fMRI study[J]. Neuroimage, 2007, 34(2): 702-713.
[12] Guillot A, Collet C, Nguyen VA, et al. Functional neuroanatomical networks associated with expertise in motor imagery[J]. Neuroimage, 2008, 41(4): 1471-1483.
[13] Kasess CH, Windischberger C, Cunnington R, et al. The suppressive influence of SMA on M1 in motor imagery revealed by fMRI and dynamic causal modeling[J]. Neuroimage, 2008, 40(2): 828-837.
[14] Gao Q, Duan X, Chen H. Evaluation of effective connectivity of motor areas during motor imagery and execution using conditional Granger causality[J]. Neuroimage, 2011, 54(2): 1280-1288.
[15] Hammer EM, Halder S, Blankertz B, et al. Psychological predictors of SMR-BCI performance[J]. Biol Psychol, 2012, 89(1): 80-86.
[16] Halder S, Varkuti B, Bogdan M, et al. Prediction of brain-computer interface aptitude from individual brain structure[J]. Front Hum Neurosci, 2013, 7: 1-9.
[17] Ahn M, Cho H, Ahn S, et al. High theta and low alpha powers may be indicative of BCI-illiteracy in motor imagery[J]. PLoS ONE, 2013, 8(11): e80886.
[18] Xu P, Hu X, Yao D. Improved wavelet entropy calculation with window functions and its preliminary application to study intracranial pressure[J]. Comput Biol Med, 2013, 43(5): 425-433.
[19] Zhang R, Xu P, Chen R, et al. Predicting inter-session performance of SMR-based brain-computer interface using the spectral entropy of resting-state EEG[J]. Brain Topogr, 2015, 28(5): 680-690.
[20] Zhang R, Yao D, Valdés-Sosa PA, et al. Efficient resting-state EEG network facilitates motor imagery performance[J]. J Neural Eng, 2015, 12(6): 066024.
[21] Gong J, Luo C, Chang X, et al. Whitematter connectivity pattern associate with characteristics of scalp EEG signals[J]. Brain Topogr, 2017, 30(6): 797-809.
[22] Nijholt A, Tan D, Pfurtscheller G, et al. Brain-computer interfacing for intelligent systems [J]. IEEE Intelligent Systems, 2008, 23(3): 72-79.
[23] Oostenveld R, Praamstra P. The five percent electrode system for high-resolution EEG and ERP measurements [J]. Clin Neurophysiol, 2001, 112(4): 713-719.
[24] Gwin JT, Ferris D. High-density EEG and independent component analysis mixture models distinguish knee contractions from ankle contractions[C]//33rd International Conference of the IEEE EMSS. Boston: IEEE, 2011:4195-4198.
[25] Meer AVD, Frederikus VDW. Only threefingers write, but the whole brain works: A high-densityEEG study showing advantages of drawing over typing forlearning [J]. Front Psychol, 2017, 8:1-9.
[26] Vukeli M, Gharabaghi A. Oscillatory entrainment of the motor cortical network during motor imagery is modulated by the feedback modality [J]. Neuroimage, 2015, 111:1-11.
[27] Leuthardt EC, Schalk G, Wolpaw JR, et al. A brain-computer interface using electrocorticographic signals in humans [J]. J Neural Eng, 2004, 1(2): 63-71.
[28] Demirer RM, Ozerdem MS, Bayrak C. Classification of imaginary movements in ECoG with a hybrid approach based on multi-dimensional Hilbert-SVM solution [J]. J Neurosci Methods, 2009, 178(1): 214-218.
[29] Blakely T, Miller KJ, Zanos SP, et al. Robust, long-term control of an electrocorticographic brain-computer interface with fixed parameters [J]. Neurosurg Focus, 2009, 27(1): E13.
[30] Schalk G, Miller KJ, Anderson NR, et al. Two-dimensional movement control using electrocorticographic signals in humans [J]. J Neural Eng, 2008, 5(1): 75-84.
[31] Hinterberger T, Widman G, Lal TN, et al. Voluntary brain regulation and communication with electrocorticogram signals [J]. Epilepsy Behav, 2008, 13(2): 300-306.
[32] Brunner P, Ritaccio AL, Emrich JF, et al. Rapid communication with a “P300” matrix speller using electrocorticographic signals (ECoG) [J]. Front Neurosci, 2011, 5(4): 5.
[33] Krusienski DJ, Sellers EW, Cabestaing F, et al. A comparison of classification techniques for the P300 Speller [J]. J Neural Eng, 2006, 3(4): 299-305.
[34] Chestek CA, Gilja V, Blabe CH, et al. Hand posture classification using electrocorticography signals in the gamma band over human sensorimotor brain areas [J]. J Neural Eng, 2013, 10(2): 026002.
[35] Kapeller C, Gergondet P, Kamada K, et al. Online control of a humanoid robot through hand movement imagination using CSP and ECoG based features[C]// Conf Proc IEEE Eng Med Biol Soc, 2015:1765-1768.
[36] D′Croz-Baron D, Ramirez JM, Baker M, et al. A BCI motor imagery experiment based on parametric feature extraction and Fisher Criterion[C]// International Conference on Electrical Communications and Computers.Cholula: IEEE, 2012:257-261.
[37] Jamaloo F, Mikaeili, M. Discriminative common spatial pattern sub-bands weighting based on distinction sensitive learning vector quantization method in motor imagery based brain-computer interface[J]. J Med Signals Sens, 2015, 5(3): 156-161.
[38] Zhang Y, Wang Y, Jin J, et al. Sparse Bayesian learning for obtaining sparsity of EEG frequency bands based feature vectors in motor imagery classification[J]. Int J Neural Syst, 2017, 27(2): 1650032.
[39] Yuan H, Doud A, Gururajan A, et al. Corticalimaging of event-related (de)synchronization during online control of brain-computer interface using minimum-norm estimates in frequency domain [J]. IEEE Trans Neural Syst Rehabil Eng, 2008, 16(5): 425-431.
[40] Edelman BJ, Baxter B, He B. EEG Source Imaging Enhances the Decoding of Complex Right Hand Motor Imagery Tasks [J]. IEEE Trans Biomed Eng, 2016, 63(1): 4-14.
[41] Ramadan R A, Vasilakos A V. Brain computer interface: control signals review[J]. Neurocomputing, 2017, 223: 26-44.
[42] Lotte F, Congedo M, Lécuyer A, et al. A review of classification algorithms for EEG-based brain-computer interfaces[J]. J Neural Eng, 2007, 4(2): R1-R13.
[43] Lei X, Yang P, Yao D. An empirical bayesian framework for brain-computer interfaces[J]. IEEE Trans Neural Syst Rehabil Eng, 2009, 17(6): 521-529.
[44] Mohamed S, Haggag S, Nahavandi S, et al. Towards automated quality assessment measure for EEG signals[J]. Neurocomputing, 2017, 237: 281-290.
[45] Hettiarachchi IT, Babaei T, Nguyen T, et al. A fresh look at functional link neural network for motor imagery-based brain-computer interface[J]. J Neurosci Methods, 2018, 305:28-35.
[46] LeCun Y, Bengio Y, Hinton G. Deep learning[J]. Nature, 2015, 521(7553): 436-444.
[47] Chiarelli AM, Croce P, Merla A, et al. Deep learning for hybrid EEG-fNIRS brain-computer interface: application to motor imagery classification[J]. J Neural Eng, 2018, 15(3): 036028.
[48] Jiao Y, Zhang Y, Chen X, et al. Sparsegroup representation model for motor imagery EEG Classification[J]. IEEE J Biomed Health Inform, 2018,2018:1.
[49] Hossain I, Khosravi A, Hettiarachchi I, et al.Multiclass informative instance transfer learning framework for motor imagery-based brain-computer interface[J]. Comput Intell Neurosci, 2018, 2018: 6323414.
[50] Dai M, Zheng D, Liu S, et al. Transfer kernel common spatial patterns for motor imagery brain-computer interface classification[J]. Comput Math Methods Med, 2018, 2018: 9871603.
[51] Pfurtscheller G, Allison BZ, Brunner C, et al. The hybrid BCI [J]. Front Neurosci, 2010, 2(30): 30.
[52] Fazli S, Mehnert J, Steinbrink J, et al. Enhanced performance by a hybrid NIRS-EEG brain computer interface [J]. Neuroimage, 2012, 59(1): 519-529.
[53] Blokland Y, Spyrou L, Thijssen D, et al. Combined EEG-fNIRS decoding of motor attempt and imagery for brain switch control: an offline study in patients with tetraplegia [J]. IEEE Trans Neural Syst Rehabil Eng, 2014, 22(2): 222-229.
[54] Fels M, Bauer R, Gharabaghi A. Predicting workload profiles of brain-robot interface and electromygraphic neurofeedback with cortical resting-state networks:Personal trait or task-specific challenge?[J]. J Neural Eng, 2015, 12(4): 046029.
[55] Ma J, Zhang Y, Cichocki A, et al. A novel EOG/EEG hybrid human-machine interface adoptingeye movements and ERPs: Application to robot control [J]. IEEE Trans Biomed Eng, 2015, 62(3): 876-889.
[56] Pfurtscheller G, Solis-Escalante T, Ortner R, et al. Self-paced operation of an SSVEP-based orthosis with and without an imagery-based “brain switch”:A feasibility study towards a hybrid BCI [J]. IEEE Trans Neural Syst Rehabil Eng, 2010, 18(4): 409-414.
[57] Cao L, Li J, Ji H, et al. A hybrid brain computer interface system based on the neurophysiological protocol and brain-actuated switch for wheelchair control [J]. J Neurosci Methods, 2014, 229(5): 33-43.
[58] Horki P, Solisescalante T, Neuper C, et al. Combined motor imagery and SSVEP based BCI control of a 2 DoF artificial upper limb [J]. Med Biol Eng Comput, 2011, 49(5): 567-577.
[59] Li Y, Long J, Yu T, et al. An EEG-based BCI system for 2-D cursor control by combining Mu/Beta rhythm and P300 potential [J]. IEEE Trans Biomed Eng, 2010, 57(10): 2495-2505.
[60] Long J, Li Y, Yu T, et al. Target selectionwith hybrid feature for BCI-based 2-D cursor control [J]. IEEE Trans Biomed Eng, 2012, 59(1): 132-140.
[61] Bhattacharyya S, Konar A, Tibarewala DN. Motor imagery, P300 and error-related EEG-based robot arm movement control for rehabilitation purpose [J]. Med Biol Eng Comput, 2014, 52(12): 1007-1017.
[62] Hwang HJ, Kim S, Choi S, et al. EEG-based brain-computer interfaces: a thorough literature survey[J]. Int J Hum Comput Interact, 2013, 29(12): 814-826.
[63] Brunner P, Schalk G. Toward gaze-independent brain-computer interfaces[J]. Clin Neurophysiol, 2013, 124(5): 831-833.
[64] Guo F, Hong B, Gao X, et al. A brain-computer interface using motion-onset visual evoked potential[J]. J Neural Eng, 2008, 5(4): 477-485.
[65] Ma T, Li H, Deng L, et al. The hybrid BCI system for movement control by combining motor imagery and moving onset visual evoked potential[J]. J Neural Eng, 2017, 14(2): 026015.
[66] Müller-Putz GR, Scherer R, Neuper C, et al. Steady-state somatosensory evoked potentials: suitable brain signals for brain-computer interfaces? [J]. IEEE Trans Neural Syst Rehabil Eng, 2006, 14(1): 30-37.
[67] Lin Y, Sheng X, Zhang D, et al. A stimulus-independent hybrid BCI based on motor imagery andsomatosensory attentional orientation [J]. IEEE Trans Neural Syst Rehabil Eng, 2017, 25(9): 1674-1682.
[68] Lin Y, Meng J, Zhang D, et al. Selectivesensation based brain-computer interface via mechanical vibrotactile stimulation [J]. PLoS ONE, 2013, 8(6): e64784.
[69] Ahn S, Ahn M, Cho H, et al. Achieving a hybrid brain-computer interface with tactile selective attention and motor imagery [J]. J Neural Eng, 2014, 11(6): 066004.
[70] Yao L, Meng J, Zhang D, et al. Combining motor imagery with selective sensation toward a hybrid-modality BCI [J]. IEEE Trans Biomed Eng, 2014, 61(8): 2304-2312.
[71] Yi W, Qiu S, Wang K, et al. Enhancing performance of a motor imagery based brain-computer interface by incorporating electrical stimulation-induced SSSEP [J]. J Neural Eng, 2016, 14(2): 026002.
[72] Tsui CSL, Gan JQ, Roberts SJ. A self-paced brain-computer interface for controlling a robot simulator: an online event labelling paradigm and an extended Kalman filter based algorithm for online training [J]. Med Biol Eng Comput, 2009, 47(3): 257-265.
[73] Leeb R, Friedman D, Müllerputz GR, et al. Self-paced (asynchronous) BCI control of a wheelchair invirtual environments: A case study with a tetraplegic [J]. Comput Intell Neurosci, 2007, 2007:79642-79650.
[74] Townsend G, Graimann B, Pfurtscheller G. Continuous EEG classification during motor imagery--simulation of an asynchronous BCI [J]. IEEE Trans Neural Syst Rehabil Eng, 2004, 12(2): 258-265.
[75] Sadeghian EB, Moradi MH. Continuous detection of motor imagery in a four-class asynchronous BCI[C]// Conf Proc IEEE Eng Med Biol Soc,Lyon: IEEE 2007:3241-3244.
[76] Pfurtscheller G, Solis-Escalante T. Could the beta rebound in the EEG be suitable to realize a “brain switch”? [J]. Clin Neurophysiol, 2009, 120(1): 24-29.
[77] Yu Y, Zhou Z, Yin E, et al. Toward brain-actuated car applications: Self-paced control with a motor imagery-based brain-computer interface [J]. Comput Biol Med, 2016, 77:148-155.
[78] 吕旭林. 基于运动想象的反馈系统实现及应用研究 [D]. 成都: 电子科技大学, 2017.
[79] Doud AJ, Lucas JP, Pisansky MT, et al. Continuous three-dimensional control of a virtual helicopterusing a motor imagery based brain-computer interface [J]. PLoS NE, 2011, 6(10): e26322.
[80] Long J, Li Y, Wang H, et al. A hybrid brain computer interface to control the direction and speed of a simulated or real wheelchair [J]. IEEE Trans Neural Syst Rehabil Eng, 2012, 20(5): 720-729.
[81] Waldert S, Preissl H, Demandt E, et al. Hand movement direction decoded from MEG and EEG [J]. J Neurosci, 2008, 28(4): 1000-1008.
[82] Zhang J, Sudre G, Li X, et al. Task-related MEG source localization via discriminant analysis [J]. Conf Proc IEEE Eng Med Biol Soc, 2011, 2011(4): 2351-2354.
[83] Yuan H, Perdoni C, He B. Relationship between speed and EEG activity during imagined and executed hand movements [J]. J Neural Eng, 2010, 7(2): 26001.
[84] Jochumsen M, Niazi IK, Mrachaczkersting N, et al. Detection and classification of movement-related cortical potentials associated with task force and speed [J]. J Neural Eng, 2013, 10(5): 056015.
[85] Nakayashiki K, Saeki M, Takata Y, et al. Modulation of event-related desynchronization during kinematic and kinetic hand movements [J]. J Neuroeng Rehabil, 2014, 11(1): 1-9.
[86] Liao K, Xiao R, Gonzalez J, et al. Decoding individual finger movements from one hand using human EEG signals [J]. PLoS ONE, 2014, 9(1): e85192.
[87] Sonkin KM, Stankevich LA, Khomenko JG, et al. Development of electroencephalographic pattern classifiers for real and imaginary thumb and index finger movements of one hand [J]. Artif Intell Med, 2015, 63(2): 107-117.
[88] Hasegawa K, Kasuga S, Takasaki K, et al. Ipsilateral EEG mu rhythm reflects the excitability of uncrossed pathways projecting to shoulder muscles[J]. J Neuroengineering Rehabil, 2017, 14(1): 85-96.
[89] Bundy DT, 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.
[90] Dodd KC, Nair VA, Prabhakaran V. Role of the contralesional vs. ipsilesional hemisphere in stroke recovery[J]. Front Hum Neurosci, 2017, 11(469): 1-9.
[91] Mohapp A, Scherer R, Keinrath C, et al. Single-trial EEG classification of executed and imagined hand movements in hemiparetic stroke patients [C]//The 3rd International Brain-Computer Workshop and Training Course.Graz: Elsevier, 2006: 80-81.
[92] Chung E, Park SI, Jang YY, et al. Effects of brain computer interface-based functional electrical stimulation on balance and gait function in patients with stroke: preliminary results[J]. J Phys Ther Sci. 2015, 27(2): 513-516.
[93] Lee YS, Bae SH, Lee SH, et al. Neurofeedback training improves the dual-task performance ability in stroke patients[J]. Tohoku J Exp Med, 2015, 236(1): 81-88.
[94] Cho W, Sabathiel N, Ortner R, et al. Pairedassociative stimulation using brain-computer interfaces for stroke rehabilitation: A pilot study [J]. Eur J Transl Myol, 2016, 26(3): 219-222.
[95] Cervera MA, Soekadar SR, Ushiba J, et al. Brain-computer interfaces for post-stroke motor rehabilitation: a meta-analysis[J]. Ann Clin Transl Neurol, 2018, 5(5): 651-663.