脑电逆问题在运动康复领域中的应用

doi:10.3969/j.issn.0258-8021.2017.06.014

Abstract
Figure/Table
References (44)
Related Citation (9)

Download: PDF (816 KB) HTML (1 KB)
Export: BibTeX | EndNote (RIS)

Abstract The EEG-based BCI rehabilitation system has been growing lots of interests in the motor rehabilitation area. However, conventional BCI paradigms limit the intuitive use of these systems. The EEG source imaging can reveal and identify the self-modulated neural activity with high spatial and time resolution thereby expand command sets used by motor rehabilitation systems. This review introduced state-of-the-art EEG source imaging methods and its applications in motor rehabilitation area. We also summarized problem and analyzed the trend that combing EEG source imaging for guiding rehabilitation in the future.

Key words： EEG source imaing motor rehabilitation motor-imagery brain-computer interface

Received: 09 February 2017

PACS:

R318

	Service

	E-mail this article
	Add to my bookshelf
	Add to citation manager
	E-mail Alert
	RSS
	Articles by authors
	Xu Lichao
	Wang Zhongpeng
	Xu Minpeng
	He Feng
	Zhou Peng
	Ming Dong
	Qi Hongzhi

Cite this article:

Xu Lichao,Wang Zhongpeng,Xu Minpeng, et al. EEG Inverse Problem and its Application in Motor Rehabilitation Area[J]. Chinese Journal of Biomedical Engineering, 2017, 36(6): 733-740.

URL:

http://cjbme.csbme.org/EN/10.3969/j.issn.0258-8021.2017.06.014 OR http://cjbme.csbme.org/EN/Y2017/V36/I6/733

[1] Plonsey R, Heppner DB. Considerations of quais-stationarity in electrophysiological systems[J].Bulletin of Mathematical Biophysics,1967,29(4):657-664.
[2] Vorwerk J, Clerc M, Burger M, et al.Comparison of boundary element and finite element approaches to the EEG forward problem[J].Biomed Tech,2012,57(Suppl.1):795-798.
[3] Schwan H,Kay C.The conductivity of living tissues[J].Annals of the New York Academy of Sciences,1957,65(6):1007-1013.
[4] Acar ZA,Acar CE,Makeig S.Simultaneous head tissue conductivity and EEG source location estimation [J].Neuro Image,2016, 124:168-180.
[5] Tikhonov AN,Arsenin VY. Solutions of ill-posed problems [J].SIAM Rev,1979,21(2):266-267.
[6] Hämäläinen MS,Ilmoniemi RJ. Interpreting measured magnetic fields of the brain: Minimum norm estimates [J]. Medical & Biological Engineering & Computing, 1994, 32:35-42.
[7] Pascual\|Marqui RD,Michel CM,Lehmann D.Low resolution electromagnetic tomography: A new method for localizing electrical activity in the brain[J].International Journal of Psychophysiology,1994,18(1):49-65.
[8] Gorodnitsky IF,George JS,Rao BD.Neuromagnetic source imaging with FOCUSS: a recursive weighted minimum norm algorithm[J]. Electroencephalography and Clinical Neurophysiology,1995,95(4):231-251.
[9] Hansen PC,O′Leary DP.The use of the L-curve in the regularization of discrete ill-posed problems[J].SIAM Journal on Scientific Computing,1993,14(6):1487-1503.
[10] Wipf D, Nagarajan S. A unified Bayesian framework for MEG/EEG source imaging[J].NeuroImage,2009,44(3):947-966.
[11] Friston K,Harrison L,Daunizeau J,et al.Multiple sparse priors for the M/EEG inverse problem[J].NeuroImage,2008,39(3):1104-1120.
[12] Phillips C,Mattout J, Rugg MD,et al.An empirical bayesian solution to the source reconstruction problem in EEG[J]. NeuroImage, 2005, 24(4):997-1011.
[13] López-Larraz E, Montesano L, Gil-Agudo Á,et al.Continuous decoding of movement intention of upper limb self-initiated analytic movements from pre-movement EEG correlates[J].Journal of Neuroengineering and Rehabilitation,2014,11(1):153.
[14] Besserve M,Martinerie J,Garnero L.Improving quantification of functional networks with EEG inverse problem:evidence from a decoding point of view[J].NeuroImage,2011,55(4):1536-1547.
[15] Qin L,Ding L,He B.Motor imagery classification by means of source analysis for brain-computer interface applications[J].Journal of Neural Engineering,2004,1(3):135-141.
[16] Kamousi B,Liu Z,He B.Classification of motor imagery tasks for brain-computer interface applications by means of two equivalent dipoles analysis[J]. IEEE Transactions on Neural Systems and Rehabilitation Engineering,2005,13(2):166-171.
[17] Kamousi B,Amini AN,He B.Classification of motor imagery by means of cortical current density estimation and Von Neumann entropy[J]. Journal of Neural Engineering,2007,4(2):17-25.
[18] de Peralta Menendez RG, Andino SG, Perez L,et al.Non-invasive estimation of local field potentials for neuroprosthesis control[J].Cognitive Processing,2005,6(1):59-64.
[19] Edelman B,Baxter B,He B. Discriminating hand gesture motor imagery tasks using cortical current density estimation[C]//The 36th Annual International Conference of the IEEE-EMBS.Chicago:IEEE,2014:1314-1317.
[20] Edelman B,Baxter B,He B.Decoding and mapping of right hand motor imagery tasks using EEG source imaging[C]//The 7th International IEEE/EMBS Conference on Neural Engineering. Milano:IEEE,2015:194-197.
[21] Edelman B,Baxter B,He B.EEG source imaging enhances the decoding of complex right-hand motorimagery tasks[J].IEEE Transactions on Biomedical Engineering,2016,63(1):4-14.
[22] Do Nascimento OF,Nielsen KD,Voigt M.Movement-related parameters modulate cortical activity during imaginary isometric plantar-flexions[J].Experimental brain research,2006,171(1):78-90.
[23] Jerbi K,Vidal J,Mattout J,et al.Coherent neural representation of hand speed in humans revealed by MEG imaging[J].Proceedings of the National Academy of Sciences,2007,104(18):7676-7681.
[24] Hammon PS,Makeig S,Poizner H,et al.Predicting reaching targets from human EEG[J].IEEE Signal Processing Magazine, 2008,25(1):69-77.
[25] Bradberry TJ,Gentili RJ,Contreras-Vidal JL.Reconstructing three-dimensional hand movements from noninvasive electroencephalographic signals[J].Journal of Neuroscience,2010,30(9):3432-3437.
[26] Seeber M,Scherer R,Wagner J,et al.EEG beta suppression and low gamma modulation are different elements of human upright walking[J].Frontiers in Human Neuroscience,2014,8:485.
[27] Seeber M,Scherer R,Wagner J,et al.High and low gamma EEG oscillations in central sensorimotor areas are conversely modulated during the human gait cycle[J].Neuroimage,2015,112:318-326.
[28] Yuan H,Doud A,Gururajan A,et al.Cortical imaging of event-related (de) synchronization during online control of brain-computer interface using minimum-norm estimates in frequency domain[J].IEEE Transactions on Neural Systems and Rehabilitation Engineering,2008,16(5):425-431.
[29] Wagner J,Solis-Escalante T, Grieshofer P, et al. Level of participation in robotic-assisted treadmill walking modulates midline sensorimotor EEG rhythms in able-bodied subjects[J]. Neuroimage, 2012, 63(3): 1203-1211.
[30] Steinisch M,Tana MG,Comani S.A post-stroke rehabilitation system integrating robotics,VR and high-resolution EEG imaging[J].IEEE Transactions on Neural Systems and Rehabilitation Engineering,2013,21(5):849-859.
[31] Eichelbaum S,Dannhauer M,Hlawitschka M,et al.Visualizing simulated electrical fields from electroencephalography and transcranial electric brain stimulation:A comparative evaluation[J]. NeuroImage, 2014, 101:513-530.
[32] Antelis JM,Montesano L,Ramos-Murguialday A,et al.Decoding upper limb movement attempt from EEG measurements of the contralesional motor cortex in chronic stroke patients[J].IEEE Transactions on Biomedical Engineering,2017,64(1):99-111.
[33] Gwin JT,Gramann K,Makeig S,et al.Electrocortical activity is coupled to gait cycle phase during treadmill walking[J]. Neuroimage, 2011, 54(2): 1289-1296.
[34] Gwin JT,Ferris DP.An EEG-based study of discrete isometric and isotonic human lower limb muscle contractions[J].Journal of Neuroengineering and Rehabilitation,2012,9(1):35.
[35] Krakowski AI,Ross LA,Snyder AC,et al.The neurophysiology of human biological motion processing: A high-density electrical mapping study[J].NeuroImage,2011,56(1):373-383.
[36] Evans N,Blanke O.Shared electrophysiology mechanisms of body ownership and motor imagery[J].Neuroimage,2013,64:216-228.
[37] Mottaz A,Solc M,Magnin C,et al.Neurofeedback training of alpha-band coherence enhances motor performance[J].Clinical Neurophysiology,2015,126(9):1754-1760.
[38] Zich C,Debener S,Kranczioch C,et al.Real-time EEG feedback during simultaneous EEG-fMRI identifies the cortical signature of motor imagery[J].Neuroimage,2015,114:438-447.
[39] Pellegrino G,Tomasevic L,Tombini M,et al.Inter-hemispheric coupling changes associate with motor improvements after robotic stroke rehabilitation[J]. Restorative Neurology and Neuroscience, 2012, 30(6):497-510.
[40] Murakami S,Okada Y.Invariance in current dipole moment density across brain structures and species: physiological constraint for neuroimaging[J].Neuroimage,2015,111:49-58.
[41] Lascano AM,Perneger T,Vulliemoz S,et al.Yield of MRI, high-density electric source imaging (HD-ESI), SPECT and PET in epilepsy surgery candidates[J].Clinical Neurophysiology,2016,127(1):150-155.
[42] Mullen T,Kothe C,Chi YM,et al.Real-time modeling and 3D visualization of source dynamics and connectivity using wearable EEG[C]// The 35th Annual International Conference of the IEEE-EMBS. Osaka:IEEE,2013:2184-2187.
[43] Stopczynski A,Stahlhut C,Larsen JE,et al.The smartphone brain scanner: a portable real-time neuroimaging system[J].PLoS ONE,2014,9(2):e86733.
[44] Juhasz Z,Kozmann G.A GPU-based soft real-time system for simultaneous EEG processing and visualization[J].Scalable Computing:Practice and Experience,2016,17(2):61-78.