Electroencephalographic motor imagery brain connectivity analysis for BCI: A review

Neural Computation

Volumn 28, Issue 6, 2016, Pages 999-1041

  Hamedi, Mahyar ^a   Salleh, Sh Hussain ^a   Noor, Alias Mohd ^a  

^a UNIVERSITI TEKNOLOGI MALAYSIA   (Malaysia)

Author keywords

[No Author keywords available]

Indexed keywords

ELECTROENCEPHALOGRAPHY; INTERFACES (COMPUTER);

BRAIN CONNECTIVITY; ELECTROENCEPHALOGRAPHIC (EEG); HEALTHY SUBJECTS; MOTOR IMAGERY; RECENT RESEARCHES; SINGLE CHANNELS; SPATIAL FEATURES; STROKE REHABILITATION;

BRAIN COMPUTER INTERFACE;

ANIMAL; BRAIN; BRAIN COMPUTER INTERFACE; ELECTROENCEPHALOGRAPHY; GUIDED IMAGERY; HUMAN; IMAGINATION; MOTOR PERFORMANCE; NERVE CELL NETWORK; PHYSIOLOGY; PROCEDURES; PSYCHOMOTOR PERFORMANCE; TRENDS;

ANIMALS; BRAIN; BRAIN-COMPUTER INTERFACES; ELECTROENCEPHALOGRAPHY; HUMANS; IMAGERY (PSYCHOTHERAPY); IMAGINATION; MOTOR SKILLS; NERVE NET; PSYCHOMOTOR PERFORMANCE;

EID: 84973338737     PISSN: 08997667     EISSN: 1530888X     Source Type: Journal    
DOI: 10.1162/NECO_a_00838     Document Type: Review

References (199)

1. Adey,W. R.,Walter, D. O., &Hendrix, C. (1961). Computer techniques in correlation and spectral analyses of cerebral slow waves during discriminative behavior. Experimental Neurology, 3(6), 501-524.
2. Anderson, C. W., Stolz, E., & Shamsunder, S. (1998). Multivariate autoregressive models for classification of spontaneous electroencephalographic signals during mental tasks. IEEE Transactions on Biomedical Engineering, 45(3), 277-286.
3. Ang, K. K., Guan, C., Chua, K. S. G., Ang, B. T., Kuah, C.W. K.,Wang, C., Zhang, H. (2011). A large clinical study on the ability of stroke patients to use an EEGbased motor imagery brain-computer interface. Clinical EEG and Neuroscience, 42(4), 253-258.
4. Annett, J. (1995). Motor imagery: Perception or action? Neuropsychologia, 33(11), 1395-1417.
5. Arroyo, S., Lesser, R. P., Gordon, B., Uematsu, S., Jackson, D., & Webber, R. (1993). Functional significance of the mu rhythm of human cortex:An electrophysiologic study with subdural electrodes. Electroencephalography and Clinical Neurophysiology, 87(3), 76-87.
6. Asensio-Cubero, J., Gan, J. Q., & Palaniappan, R. (2013). Multiresolution analysis over simple graphs for brain computer interfaces. Journal of Neural Engineering, 10(4), 046014.
7. Astolfi, L., Cincotti, F., Mattia, D., Marciani, M. G., Baccala, L. A., de Vico Fallani, F., Ding, L. (2007). Comparison of different cortical connectivity estimators for high-resolution EEG recordings. Human Brain Mapping, 28(2), 143-157.
8. Astolfi, L., De Vico Fallani, F., Cincotti, F., Mattia, D., Marciani, M., Bufalari, S., Edgar, J. (2007). Imaging functional brain connectivity patterns from highresolution EEG and fMRI via graph theory. Psychophysiology, 44(6), 880-893.
9. Athanasiou, A., Lithari, C., Kalogianni, K., Klados, M. A., & Bamidis, P. D. (2012). Source detection and functional connectivity of the sensorimotor cortex during actual and imaginary limb movement: A preliminary study on the implementation of econnectome in motor imagery protocols. Advances in Human-Computer Interaction, 2012, 2.
10. Baillet, S.,Mosher, J. C., & Leahy, R. M. (2001). Electromagnetic brain mapping. IEEE Signal Processing Magazine, 18(6), 14-30.
11. Bakker, M., Overeem, S., Snijders, A., Borm, G., Van Elswijk, G., Toni, I., & Bloem, B. (2008). Motor imagery of foot dorsiflexion and gait: Effects on corticospinal excitability. Clinical Neurophysiology, 119(11), 2519-2527.
12. Bashashati, A., Fatourechi, M., Ward, R. K., & Birch, G. E. (2007). A survey of signal processing algorithms in brain-computer interfaces based on electrical brain signals. Journal of Neural Engineering, 4(2), R32.
13. Beisteiner, R.,Höllinger, P., Lindinger, G., Lang,W.,&Berthoz,A. (1995). Mental representations of movements: Brain potentials associated with imagination of hand movements. Electroencephalography and Clinical Neurophysiology/Evoked Potentials Section, 96(2), 183-193.
14. Bendat, J. S., & Piersol, A. G. (1980). Engineering applications of correlation and spectral analysis. New York:Wiley-Interscience.
15. Bendat, J. S.,&Piersol, A.G. (2011). Random data: Analysis and measurement procedures. Hoboken, NJ: Wiley.
16. Bhattacharya, J., Pereda, E., & Petsche, H. (2003). Effective detection of coupling in short and noisy bivariate data. Systems, Man, and Cybernetics, Part B: IEEE Transactions on Cybernetics, 33(1), 85-95.
17. Bhattacharya, J., Petsche, H., Feldmann, U., & Rescher, B. (2001). EEG gammaband phase synchronization between posterior and frontal cortex during mental rotation in humans. Neuroscience Letters, 311(1), 29-32.
18. Billinger, M., Brunner, C., & Müller-Putz, G. R. (2013). Single-trial connectivity estimation for classification of motor imagery data. Journal of Neural Engineering, 10(4), 046006.
19. Birbaumer, N., & Cohen, L. G. (2007). Brain-computer interfaces: Communication and restoration of movement in paralysis. Journal of Physiology, 579(3), 621-636.
20. Blinowska, K. J., Kuś, R., & Kamiński, M. (2004). Granger causality and information flow in multivariate processes. Physical Review E, 70(5), 050902.
21. Boecker, H., Ceballos-Baumann, A., Bartenstein, P., Dagher, A., Forster, K., Haslinger, B., Conrad, B. (2002). AH 2 15 O positron emission tomography study on mental imagery of movement sequences-the effect of modulating sequence length and direction. Neuroimage, 17(2), 999-1009.
22. Brazier, M. A., & Casby, J. U. (1952). Crosscorrelation and autocorrelation studies of electroencephalographic potentials. Electroencephalography and Clinical Neurophysiology, 4(2), 201-211.
23. Broetz, D., Braun, C., Weber, C., Soekadar, S. R., Caria, A., & Birbaumer, N. (2010). Combination of brain-computer interface training and goal-directed physical therapy in chronic stroke: A case report. Neurorehabilitation and Neural Repair, 27, 674-679.
24. Brunner, C., Scherer, R., Graimann, B., Supp, G., & Pfurtscheller, G. (2006). Online control of a brain-computer interface using phase synchronization. IEEE Transactions on Biomedical Engineering, 53(12), 2501-2506.
25. Bullock, T., McClune, M., Achimowicz, J., Iragui-Madoz, V., Duckrow, R., & Spencer, S. (1995). EEG coherence has structure in the millimeter domain: Subdural and hippocampal recordings from epileptic patients. Electroencephalography and Clinical Neurophysiology, 95(3), 161-177.
26. Caria, A.,Weber, C., Brötz, D., Ramos, A., Ticini, L. F., Gharabaghi, A., Birbaumer, N. (2011). Chronic stroke recovery after combined BCI training and physiotherapy:A case report. Psychophysiology, 48(4), 578-582.
27. Chakravarthy, V. S., Joseph, D., & Bapi, R. S. (2010). What do the basal ganglia do? A modeling perspective. Biological Cybernetics, 103(3), 237-253.
28. Chen, D., Li, H., Yang, Y., & Chen, J. (2012). Causal connectivity brain network: A novel method of motor imagery classification for brain-computer interface applications. Paper presented at the 2012 International Conference on Computing, Measurement, Control and Sensor Network.
29. Cheung, B.L.P., Riedner, B. A., Tononi, G.,&vanVeen, B. (2010). Estimation of cortical connectivity from EEG using state-space models. IEEE Transactions on Biomedical Engineering, 57(9), 2122-2134.
30. Chiang, J., Wang, Z. J., & McKeown, M. J. (2012). A generalized multivariate autoregressive (GmAR)-based approach for EEG source connectivity analysis. IEEE Transactions on Signal Processing, 60(1), 453-465.
31. Chung, Y. G., Kim, M.-K., & Kim, S.-P. (2011). Inter-channel connectivity of motor imagery EEG signals for a noninvasive BCI application. Paper presented at 2011 InternationalWorkshop on the Pattern Recognition in NeuroImaging.
32. Cicinelli, P., Marconi, B., Zaccagnini, M., Pasqualetti, P., Filippi, M. M., & Rossini, P. M. (2006). Imagery-induced cortical excitability changes in stroke: A transcranial magnetic stimulation study. Cerebral Cortex, 16(2), 247-253.
33. Cincotti, F., Mattia, D., Aloise, F., Bufalari, S., Astolfi, L., Fallani, F. D. V., Gao, S. (2008). High-resolution EEG techniques for brain-computer interface applications. Journal of Neuroscience Methods, 167(1), 31-42.
34. Cona, F., Zavaglia, M., Astolfi, L., Babiloni, F., & Ursino, M. (2009). Changes in EEG power spectral density and cortical connectivity in healthy and tetraplegic patients during a motor imagery task. Computational Intelligence and Neuroscience, 2009, 1-12.
35. Da Silva, F. L., Hoeks, A., Smits, H., & Zetterberg, L. (1974). Model of brain rhythmic activity. Kybernetik, 15(1), 27-37.
36. Daly, I., Nasuto, S. J., & Warwick, K. (2012). Brain computer interface control via functional connectivity dynamics. Pattern Recognition, 45(6), 2123-2136.
37. Daunizeau, J., Kiebel, S. J., & Friston, K. J. (2009). Dynamic causal modelling of distributed electromagnetic responses. Neuroimage, 47(2), 590-601.
38. Decety, J., Perani, D., Jeannerod, M., Bettinardi, V., Tadary, B., Woods, R., Fazio, F. (1994). Mapping motor representations with positron emission tomography. Nature, 371, 600-602.
39. Dechent, P., Merboldt, K.-D., & Frahm, J. (2004). Is the human primary motor cortex involved in motor imagery? Cognitive Brain Research, 19(2), 138-144.
40. Deecke, L. (1996). Planning, preparation, execution, and imagery of volitional action. Cognitive Brain Research, 3(2), 59-64.
41. Deiber, M.-P., Ibanez, V., Honda, M., Sadato, N., Raman, R., & Hallett, M. (1998). Cerebral processes related to visuomotor imagery and generation of simple finger movements studied with positron emission tomography. Neuroimage, 7(2), 73-85.
42. Delorme, A., Mullen, T., Kothe, C., Acar, Z. A., Bigdely-Shamlo, N., Vankov, A., & Makeig, S. (2011). EEGLAB, SIFT, NFT, BCILAB, and ERICA: New tools for advanced EEG processing. Computational Intelligence and Neuroscience, 2011, 10.
43. Ding,M., Bressler, S. L., Yang,W.,&Liang,H. (2000). Short-window spectral analysis of cortical event-related potentials by adaptivemultivariate autoregressive modeling:Data preprocessing,model validation, and variability assessment. Biological Cybernetics, 83(1), 35-45.
44. Dornhege, G., Blankertz, B., Curio, G., & Müller, K.-R. (2004). Boosting bit rates in noninvasive EEG single-trial classifications by feature combination and multiclass paradigms. IEEE Transactions on Biomedical Engineering, 51(6), 993-1002.
45. Doya, K. (2000). Complementary roles of basal ganglia and cerebellum in learning and motor control. Current Opinion in Neurobiology, 10(6), 732-739.
46. Dunsky, A., Dickstein, R., Ariav, C., Deutsch, J., & Marcovitz, E. (2006). Motor imagery practice in gait rehabilitation of chronic post-stroke hemiparesis: Four case studies. International Journal of Rehabilitation Research, 29(4), 351-356.
47. Dunsky, A., Dickstein, R., Marcovitz, E., Levy, S., & Deutsch, J. (2008). Home-based motor imagery training for gait rehabilitation of people with chronic poststroke hemiparesis. Archives of Physical Medicine and Rehabilitation, 89(8), 1580-1588.
48. Eichler, M. (2007). Causal inference from time series: What can be learned from Granger causality? In Proceedings of the 13th International Congress of Logic, Methodology and Philosophy of Science. London: King's College Publications.
49. Erla, S., Faes, L., Tranquillini, E., Orrico, D., & Nollo, G. (2009). Multivariate autoregressive model with instantaneous effects to improve brain connectivity estimation. International Journal of Bioelectromagnetism, 11(2), 74-79.
50. Ersland, L., Rosén, G., Lundervold, A., Smievoll, A. I., Tillung, T., & Sundberg, H. (1996). Phantom limb imaginary fingertapping causes primary motor cortex activation: An fMRI study. Neuroreport, 8(1), 207-210.
51. Faes, L., & Nollo, G. (2010). Extended causal modeling to assess partial directed coherence in multiple time series with significant instantaneous interactions. Biological Cybernetics, 103(5), 387-400.
52. Fallani, F. D. V., Astolfi, L., Cincotti, F., Babiloni, F., & Colosimo, A. (2008). Characterization of cortical networks during motor tasks in humans. Biophysics and Bioengineering Letters, 1(3), 1-13.
53. Farah, M. J. (1984). The neurological basis of mental imagery: A componential analysis. Cognition, 18(1), 245-272.
54. Fingelkurts, A. A., Fingelkurts,A.A.,&Kähkönen, S. (2005). Functional connectivity in the brain-is it an elusive concept? Neuroscience and Biobehavioral Reviews, 28(8), 827-836.
55. Finger, S. (2001). Origins of neuroscience: A history of explorations into brain function. New York: Oxford University Press.
56. Finke, A., Lenhardt, A., & Ritter, H. (2009). The MindGame: A P300-based brain- computer interface game. Neural Networks, 22(9), 1329-1333.
57. Franaszczuk, P. J.,&Bergey,G.K. (1998). Application of the directed transfer function method to mesial and lateral onset temporal lobe seizures. Brain Topography, 11(1), 13-21.
58. Friston, K. J., Harrison, L., & Penny,W. (2003). Dynamic causal modelling. NeuroImage, 19(4), 1273-1302.
59. Geweke, J. (1982). Measurement of linear dependence and feedback between multiple time series. Journal of the American Statistical Association, 77(378), 304-313.
60. Geweke, J. F. (1984). Measures of conditional linear dependence and feedback between time series. Journal of the American Statistical Association, 79(388), 907-915.
61. Ginter Jr, J., Blinowska, K., Kamin, M., Durka, P., Pfurtscheller, G., & Neuper, C. (2005). Propagation of EEG activity in the beta and gamma band during movement imagery in humans. Methods Inf. Med., 44(1), 106-113.
62. Ginter, J., Blinowska, K., Kamiński, M., & Durka, P. (2001). Phase and amplitude analysis in time-frequency space-application to voluntary finger movement. Journal of Neuroscience Methods, 110(1), 113-124.
63. Goldenberg, G., Podreka, I., Steiner, M., & Willmes, K. (1987). Patterns of regional cerebral blood flow related to memorizing of high and low imagery words-an emission computer tomography study. Neuropsychologia, 25(3), 473-485.
64. Gómez-Herrero, G., Atienza, M., Egiazarian, K., & Cantero, J. L. (2008). Measuring directional coupling between EEG sources. Neuroimage, 43(3), 497-508.
65. Gonuguntla, V.,Wang, Y., & Veluvolu, K. C. (2013). Phase synchrony in subject-specific reactive band of EEG for classification of motor imagery tasks. Paper presented at the Engineering in Medicine and Biology Society, 2013 35th Annual International Conference of the IEEE.
66. Graimann, B., Allison, B., Mandel, C., Lüth, T., Valbuena, D., & Gräser, A. (2008). Non-invasive brain-computer interfaces for semi-autonomous assistive devices. In A. Schuster (Ed.), Robust intelligent systems (pp. 113-138). New York: Springer.
67. Graimann, B., Allison, B. Z., & Pfurtscheller, G. (2010). Brain-computer interfaces: Revolutionizing human-computer interaction. New York: Springer Science & Business Media.
68. Granger, C. W. (1969). Investigating causal relations by econometric models and cross-spectral methods. Econometrica: Journal of the Econometric Society, 37, 424-438.
69. Groß, J., Kujala, J., Hämäläinen, M., Timmermann, L., Schnitzler, A., & Salmelin, R. (2001). Dynamic imaging of coherent sources: Studying neural interactions in the human brain. Proceedings of the National Academy of Sciences, 98(2), 694-699.
70. Grosse-Wentrup,M. (2009). Understanding brain connectivity patterns during motor imagery for brain-computer interfacing. In D. Koller, D. Schurmans, Y. Bengio, & L. Bottou (Eds.), Advances in neural information processing systems, 21. Cambridge, MA: MIT Press.
71. Gysels, E., & Celka, P. (2004). Phase synchronization for the recognition of mental tasks in a brain-computer interface. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 12(4), 406-415.
72. Gysels, E., Renevey, P., & Celka, P. (2005). SVM-based recursive feature elimination to compare phase synchronization computed from broadband and narrowband EEG signals in brain-computer interfaces. Signal Processing, 85(11), 2178-2189.
73. Hallett, M., Fieldman, J., Cohen, L. G., Sadato, N., & Pascual-Leone, A. (1994). Involvement of primary motor cortex in motor imagery and mental practice. Behavioral and Brain Sciences, 17(02), 210-210.
74. Hamedi, M., Salleh, S.-H., Ting, C.-M., Samdin, S. B., & Mohd Noor, A. (2015). Sensor space time-varying information flow analysis ofmulticlassmotor imagery through Kalman Smoother and EM algorithm. Paper presented at the 2015 International Conference on BioSignal Analysis, Processing and Systems.
75. Hanakawa, T., Dimyan, M. A., & Hallett, M. (2008). Motor planning, imagery, and execution in the distributed motor network: A time-course study with functional MRI. Cerebral Cortex, 18(12), 2775-2788.
76. Hinterberger, T., Birbaumer, N., & Flor, H. (2005). Assessment of cognitive function and communication ability in a completely locked-in patient. Neurology, 64(7), 1307-1308.
77. Hoang, T., Tran, D.,Nguyen, P.,Huang, X., & Sharma, D. (2011). Experiments on using combined shortwindow bivariate autoregression forEEGclassification. Paper presented at the 2011 5th International IEEE/EMBS Conference on Neural Engineering.
78. Hoke,M., Lehnertz, K., Pantev, C., & Lütkenhöner, B. (1989). Spatiotemporal aspects of synergetic processes in the auditory cortex as revealed by the magnetoencephalogram. In E. Basar & T. H. Bullock (Eds.), Brain Dynamics (pp. 84-105). New York: Springer.
79. Hosoya, Y. (2001). Elimination of third-series effect and defining partial measures of causality. Journal of Time Series Analysis, 22(5), 537-554.
80. Hu, S., Wang, H., Zhang, J., Kong, W., & Cao, Y. (2014). Causality from Cz to C3/C4 or between C3 and C4 revealed by Granger causality and new causality during motor imagery. International Joint Conference on Paper presented at the Neural Networks.
81. Huan, N.-J., & Palaniappan, R. (2004). Neural network classification of autoregressive features from electroencephalogram signals for brain-computer interface design. Journal of Neural Engineering, 1(3), 142.
82. Hyvärinen, A., Zhang, K., Shimizu, S.,&Hoyer,P. O. (2010). Estimation of a structural vector autoregression model using non-gaussianity. Journal of Machine Learning Research, 11, 1709-1731.
83. Ioannides, A. A. (2007). Dynamic functional connectivity. Current Opinion in Neurobiology, 17(2), 161-170.
84. Jeannerod, M., & Decety, J. (1995). Mental motor imagery: A window into the representational stages of action. Current Opinion in Neurobiology, 5(6), 727-732.
85. Jeong, J., Gore, J. C., & Peterson, B. S. (2001). Mutual information analysis of the EEG in patients with Alzheimer's disease. Clinical Neurophysiology, 112(5), 827-835.
86. John, E. R. (2002). The neurophysics of consciousness. Brain Research Reviews, 39(1), 1-28.
87. Johnson, S. H. (2000). Imagining the impossible: intact motor representations in hemiplegics. Neuroreport, 11(4), 729-732.
88. Kaiser, A., & Schreiber, T. (2002). Information transfer in continuous processes. Physica D: Nonlinear Phenomena, 166(1), 43-62.
89. Kam, T.-E., Suk, H.-I., & Lee, S.-W. (2013). Non-homogeneous spatial filter optimization for electroencephalogram (EEG)-based motor imagery classification. Neurocomputing, 108, 58-68.
90. Kaminski, M., & Blinowska, K. J. (1991). A new method of the description of the information flow in the brain structures. Biological Cybernetics, 65(3), 203-210.
91. Kamiński, M., Blinowska, K., & Szelenberger, W. (1997). Topographic analysis of coherence and propagation of EEG activity during sleep and wakefulness. Electroencephalography and Clinical Neurophysiology, 102(3), 216-227.
92. Kamiński, M., Ding, M., Truccolo, W. A., & Bressler, S. L. (2001). Evaluating causal relations in neural systems: Granger causality, directed transfer function and statistical assessment of significance. Biological Cybernetics, 85(2), 145-157.
93. Kaplan, A. Y., Fingelkurts, A. A., Fingelkurts, A. A., Ivashko, R., & Darkhovsky, B. (1997). Topological mapping of sharp reorganization synchrony in multichannel EEG. American Journal of Electroneurodiagnostic Technology, 37(4), 265-275.
94. Kiebel, S. J., Garrido, M. I., Moran, R., Chen, C. C., & Friston, K. J. (2009). Dynamic causal modeling for EEG and MEG. Human Brain Mapping, 30(6), 1866-1876.
95. Kopell, N., Ermentrout, G., Whittington, M., & Traub, R. (2000). Gamma rhythms and beta rhythms have different synchronization properties. Proceedings of the National Academy of Sciences, 97(4), 1867-1872.
96. Kosslyn, S.M., Ganis, G., & Thompson,W. L. (2001). Neural foundations of imagery. Nature Reviews Neuroscience, 2(9), 635-642.
97. Kotchoubey, B., Lang, S., Winter, S., & Birbaumer, N. (2003). Cognitive processing in completely paralyzed patients with amyotrophic lateral sclerosis. European Journal of Neurology, 10(5), 551-558.
98. Krusienski, D. J., McFarland, D. J., & Wolpaw, J. R. (2012). Value of amplitude, phase, and coherence features for a sensorimotor rhythm-based brain-computer interface. Brain Research Bulletin, 87(1), 130-134.
99. Kübler, A.,&Birbaumer, N. (2008). Brain-computer interfaces and communication in paralysis: Extinction of goal directed thinking in completely paralysed patients? Clinical Neurophysiology, 119(11), 2658-2666.
100. Kuś, R., Ginter, J. S., & Blinowska, K. J. (2005). Propagation of EEG activity during finger movement and its imagination. Acta Neurobiologiae Experimentalis, 66(3), 195-206.
101. Kuś, R., Kamiński,M.,&Blinowska, K. J. (2004). Determination of EEG activity propagation:Pair-wise versus multichannel estimate. Biomedical Engineering, IEEE Transactions on, 51(9), 1501-1510.
102. Lachaux, J.-P., Rodriguez, E., Martinerie, J., & Varela, F. J. (1999). Measuring phase synchrony in brain signals. Human Brain Mapping, 8(4), 194-208.
103. Lal, T. N., Schröder, M., Hinterberger, T., Weston, J., Bogdan, M., Birbaumer, N., & Schölkopf, B. (2004). Support vector channel selection in BCI. IEEE Transactions on Biomedical Engineering, 51(6), 1003-1010.
104. Lang, E. W., Tomé, A., Keck, I. R., Górriz-Sáez, J., & Puntonet, C. (2012). Brain connectivity analysis: A short survey. Computational Intelligence and Neuroscience, 2012, 8.
105. Lang,W., Cheyne, D., Höllinger, P., Gerschlager,W., & Lindinger, G. (1996). Electric and magnetic fields of the brain accompanying internal simulation of movement. Cognitive Brain Research, 3(2), 125-129.
106. Le, J., Menon, V., & Gevins, A. (1994). Local estimate of surface Laplacian derivation on a realistically shaped scalp surface and its performance on noisy data. Electroencephalography and Clinical Neurophysiology/Evoked Potentials Section, 92(5), 433-441.
107. Leeb, R., Friedman, D.,Müller-Putz, G. R., Scherer, R., Slater,M., & Pfurtscheller, G. (2007). Self-paced (asynchronous) BCI control of a wheelchair in virtual environments:A case study with a tetraplegic. Computational intelligence and neuroscience, 2007, art. 7.
108. Li, J., Ji, H., Cao, L., Gu, R., Xia, B., & Huang, Y. (2013). Wheelchair control based on multimodal brain-computer interfaces. In M. Lee, A. Hirose, Z.-G. Hou, & R. M. Kil (Eds.), Neural information processing, LNCS 8226 (pp. 434-441). New York: Springer.
109. Li, X., Ong, S.-H., Pan, Y., & Ang, K. K. (2013). Connectivity pattern modeling of motor imagery EEG. Paper presented at the 2013 IEEE Symposium on Computational Intelligence, Cognitive Algorithms, Mind, and Brain.
110. Lorenz, E. N. (1963). Deterministic nonperiodic flow. Journal of the Atmospheric Sciences, 20(2), 130-141.
111. Lotte, F., Congedo,M., Lécuyer, A., & Lamarche, F. (2007). A review of classification algorithms for EEG-based brain-computer interfaces. Journal of Neural Engineering, 4, 1-24.
112. Lotze, M., Montoya, P., Erb, M., Hülsmann, E., Flor, H., Klose, U., Grodd, W. (1999). Activation of cortical and cerebellar motor areas during executed and imagined hand movements: An fMRI study. Journal of Cognitive Neuroscience, 11(5), 491-501.
113. Malouin, F., Richards, C., Durand, A., & Doyon, J. (2008). Clinical assessment of motor imagery after stroke. Neurorehabilitation and Neural Repair, 22, 330-340.
114. Malouin, F., Richards, C. L., Jackson, P. L., Dumas, F., & Doyon, J. (2003). Brain activations during motor imagery of locomotor-related tasks: A PET study. Human Brain Mapping, 19(1), 47-62.
115. McEvoy, L. K., Smith, M. E., & Gevins, A. (1998). Dynamic cortical networks of verbal and spatial working memory: Effects of memory load and task practice. Cerebral Cortex, 8(7), 563-574.
116. McFarland, D. J., & Wolpaw, J. R. (2008). Brain-computer interface operation of robotic and prosthetic devices. Computer (10), 52-56.
117. Michelon, P., Vettel, J. M., & Zacks, J. M. (2006). Lateral somatotopic organization during imagined and prepared movements. Journal of Neurophysiology, 95(2), 811-822.
118. Millan, J. R., Renkens, F., Mouriño, J., & Gerstner, W. (2004). Noninvasive brainactuated control of amobile robot by human EEG. IEEE Transactions on Biomedical Engineering, 51(6), 1026-1033.
119. Mima, T., Matsuoka, T., & Hallett, M. (2000). Functional coupling of human right and left cortical motor areas demonstrated with partial coherence analysis. Neuroscience Letters, 287(2), 93-96.
120. Miranda, E. R. (2010). Plymouth brain-computer music interfacing project: FromEEG audio mixers to composition informed by cognitive neuroscience. International Journal of Arts and Technology, 3(2-3), 154-176.
121. Miyai, I., Tanabe, H. C., Sase, I., Eda, H., Oda, I., Konishi, I., Kubota, K. (2001). Cortical mapping of gait in humans: A near-infrared spectroscopic topography study. NeuroImage, 14(5), 1186-1192.
122. Moran, R. J., Kiebel, S. J., Stephan, K., Reilly, R., Daunizeau, J., & Friston, K. J. (2007). A neural mass model of spectral responses in electrophysiology. NeuroImage, 37(3), 706-720.
123. Mormann, F., Lehnertz, K., David, P., & Elger, C. E. (2000). Mean phase coherence as a measure for phase synchronization and its application to the EEG of epilepsy patients. Physica D: Nonlinear Phenomena, 144(3), 358-369.
124. Morris, C., & Lecar, H. (1981). Voltage oscillations in the barnacle giant muscle fiber. Biophysical Journal, 35(1), 193.
125. Müller-Gerking, J., Pfurtscheller, G., & Flyvbjerg, H. (1999). Designing optimal spatial filters for single-trial EEG classification in a movement task. Clinical Neurophysiology, 110(5), 787-798.
126. Müller-Putz, G. R., & Pfurtscheller, G. (2008). Control of an electrical prosthesis with an SSVEP-based BCI. IEEE Transactions on Biomedical Engineering, 55(1), 361-364.
127. Munzert, J., Lorey, B., & Zentgraf, K. (2009). Cognitive motor processes: The role of motor imagery in the study of motor representations. Brain Research Reviews, 60(2), 306-326.
128. Na, S. H., Jin, S.-H., Kim, S. Y., & Ham, B.-J. (2002). EEG in schizophrenic patients:Mutual information analysis. Clinical Neurophysiology, 113(12), 1954-1960.
129. Naeem, M., Brunner, C., Leeb, R., Graimann, B., & Pfurtscheller, G. (2006). Seperability of four-classmotor imagery data using independent components analysis. Journal of Neural Engineering, 3(3), 208.
130. Neuper, C., & Pfurtscheller, G. (1999). Motor imagery and ERD. Event-Related Desynchronization, 6, 303-325.
131. Neuper, C., & Pfurtscheller, G. (2001a). Event-related dynamics of cortical rhythms:Frequency-specific features and functional correlates. International Journal of Psychophysiology, 43(1), 41-58.
132. Neuper, C., & Pfurtscheller, G. (2001b). Evidence for distinct beta resonance frequencies in human EEG related to specific sensorimotor cortical areas. Clinical Neurophysiology, 112(11), 2084-2097.
133. Nikouline, V. V., Linkenkaer-Hansen, K., Wikström, H., Kesäniemi, M., Antonova, E. V., Ilmoniemi, R. J., & Huttunen, J. (2000). Dynamics of mu-rhythm suppression caused by median nerve stimulation: A magnetoencephalographic study in human subjects. Neuroscience letters, 294(3), 163-166.
134. Nolte, G., Bai, O., Wheaton, L., Mari, Z., Vorbach, S., & Hallett, M. (2004). Identifying true brain interaction from EEG data using the imaginary part of coherency. Clinical Neurophysiology, 115(10), 2292-2307.
135. Nolte, G., Ziehe, A., Nikulin, V. V., Schlögl, A., Krämer, N., Brismar, T., & Müller, K.-R. (2008). Robustly estimating the flow direction of information in complex physical systems. Physical Review Letters, 100(23), 234101.
136. Nunez, P. L. (2000). Toward a quantitative description of large-scale neocortical dynamic function and EEG. Behavioral and Brain Sciences, 23(3), 371-398.
137. Nunez, P. L.,& Srinivasan, R. (2006). Electric fields of the brain: The neurophysics of EEG. New York: Oxford University Press.
138. Nunez, P. L., Srinivasan, R., Westdorp, A. F., Wijesinghe, R. S., Tucker, D. M., Silberstein, R. B., & Cadusch, P. J. (1997). EEG coherency: I: Statistics, reference electrode, volume conduction, Laplacians, cortical imaging, and interpretation at multiple scales. Electroencephalography and Clinical Neurophysiology, 103(5), 499-515.
139. Ofner, P., Müller-Putz, G., Neuper, C., & Brunner, C. (2011). Comparison of feature extraction methods for brain-computer interfaces.
140. Omidvarnia, A., Azemi, G., Boashash, B., O'Toole, J. M., Colditz, P. B., & Vanhatalo, S. (2014). Measuring time-varying information flow in scalp EEG signals: Orthogonalized partial directed coherence. IEEE Transactions on Biomedical Engineering, 61(3), 680-693.
141. Pecora, L. M., & Carroll, T. L. (1990). Synchronization in chaotic systems. Physical Review Letters, 64(8), 821.
142. Pereda, E., Quiroga, R. Q., & Bhattacharya, J. (2005). Nonlinearmultivariate analysis of neurophysiological signals. Progress in Neurobiology, 77(1), 1-37.
143. Pereda, E., Rial,R., Gamundi, A.,&Gonzalez, J. (2001). Assessment of changing interdependencies between human electroencephalograms using nonlinear methods. Physica D: Nonlinear Phenomena, 148(1), 147-158.
144. Pfurtscheller, G., & Andrew, C. (1999). Event-related changes of band power and coherence: Methodology and interpretation. Journal of Clinical Neurophysiology, 16(6), 512.
145. Pfurtscheller, G.,Graimann, B.,Huggins, J. E., Levine, S. P.,&Schuh, L. A. (2003). Spatiotemporal patterns of beta desynchronization and gamma synchronization in corticographic data during self-paced movement. Clinical Neurophysiology, 114(7), 1226-1236.
146. Pfurtscheller, G., Müller, G. R., Pfurtscheller, J., Gerner, H. J., & Rupp, R. (2003). "Thought"-control of functional electrical stimulation to restore hand grasp in a patient with tetraplegia. Neuroscience Letters, 351(1), 33-36.
147. Pfurtscheller, G., & Neuper, C. (1994). Event-related synchronization of mu rhythm in the EEG over the cortical hand area in man. Neuroscience Letters, 174(1), 93-96.
148. Pfurtscheller, G.,&Neuper,C. (1997). Motor imagery activates primary sensorimotor area in humans. Neuroscience Letters, 239(2), 65-68.
149. Pfurtscheller, G., & Neuper, C. (2001). Motor imagery and direct brain-computer communication. Proceedings of the IEEE, 89(7), 1123-1134.
150. Pfurtscheller, G., Stancak, A., & Edlinger, G. (1997). On the existence of different types of central beta rhythms below 30 Hz. Electroencephalography and Clinical Neurophysiology, 102(4), 316-325.
151. Pikovsky, A. (1984). On the interaction of strange attractors. Zeitschrift für Physik B Condensed Matter, 55(2), 149-154.
152. Porro, C. A., Francescato, M. P., Cettolo, V., Diamond, M. E., Baraldi, P., Zuiani, C., Di Prampero, P. E. (1996). Primary motor and sensory cortex activation during motor performance and motor imagery: A functional magnetic resonance imaging study. Journal of Neuroscience, 16(23), 7688-7698.
153. Qin, L., Ding, L., & He, B. (2004). Motor imagery classification by means of source analysis for brain-computer interface applications. Journal of Neural Engineering, 1(3), 135.
154. Ramoser, H., Muller-Gerking, J., & Pfurtscheller, G. (2000). Optimal spatial filtering of single trial EEG during imagined hand movement. IEEE Transactions on Rehabilitation Engineering, 8(4), 441-446.
155. Rao, S. M., Binder, J., Bandettini, P., Hammeke, T., Yetkin, F., Jesmanowicz, A., Estkowski, L. (1993). Functional magnetic resonance imaging of complex human movements. Neurology, 43(11), 2311-2311.
156. Rodriguez, E., George, N., Lachaux, J.-P., Martinerie, J., Renault, B., & Varela, F. J. (1999). Perception's shadow: Long-distance synchronization of human brain activity. Nature, 397(6718), 430-433.
157. Roland, P. E., Larsen, B., Lassen, N., & Skinhoj, E. (1980). Supplementary motor area and other cortical areas in organization of voluntary movements in man. Journal of Neurophysiology, 43(1), 118-136.
158. Roth,M., Decety, J., Raybaudi,M., Massarelli, R., Delon-Martin, C., Segebarth, C., Jeannerod, M. (1996). Possible involvement of primary motor cortex in mentally simulated movement: A functional magnetic resonance imaging study. Neuroreport, 7(7), 1280-1284.
159. Sakkalis, V. (2011). Review of advanced techniques for the estimation of brain connectivity measured with EEG/MEG. Computers in Biology and Medicine, 41(12), 1110-1117.
160. Sakkalis, V., Giurcaneanu, C. D., Xanthopoulos, P., Zervakis, M. E., Tsiaras, V., Yang, Y., Micheloyannis, S. (2009). Assessment of linear and nonlinear synchronization measures for analyzing EEG in a mild epileptic paradigm. IEEE Transactions on Information Technology in Biomedicine, 13(4), 433-441.
161. Sakkalis, V., & Zervakis, M. (2009). Linear and nonlinear synchronization analysis and visualization during altered states of consciousness. INTECH Open Access Publisher.
162. Salmelin, R., & Hari, R. (1994). Spatiotemporal characteristics of sensorimotor neuromagnetic rhythms related to thumb movement. Neuroscience, 60(2), 537-550.
163. Samdin, S. B., Ting, C.-M., Salleh, S.-H., Hamedi, M., & Mohd Noor, A. (2014). Estimating dynamic cortical connectivity from motor imagery EEG using KALMAN smoother and EM algorithm. Paper presented at the 2014 IEEE Workshop on Statistical Signal Processing.
164. Sameshima, K., & Baccalá, L. A. (1999). Using partial directed coherence to describe neuronal ensemble interactions. Journal of Neuroscience Methods, 94(1), 93-103.
165. Sarnthein, J., Petsche, H., Rappelsberger, P., Shaw, G., & Von Stein, A. (1998). Synchronization between prefrontal and posterior association cortex during human working memory. Proceedings of the NationalAcademy of Sciences, 95(12), 7092-7096.
166. Sarvas, J. (1987). Basic mathematical and electromagnetic concepts of the biomagnetic inverse problem. Physics in Medicine and Biology, 32(1), 11.
167. Schlögl, A., Lee, F., Bischof, H., & Pfurtscheller, G. (2005). Characterization of fourclass motor imagery EEG data for the BCI-competition 2005. Journal of Neural Engineering, 2(4), L14.
168. Schnitzler, A., Salenius, S., Salmelin, R., Jousmäki, V., & Hari, R. (1997). Involvement of primary motor cortex in motor imagery: A neuromagnetic study. NeuroImage, 6(3), 201-208.
169. Singer, W. (1993). Synchronization of cortical activity and its putative role in information processing and learning. Annual Review of Physiology, 55(1), 349-374.
170. Sirigu, A., Duhamel, J.-R., Cohen, L., Pillon, B., Dubois, B., & Agid, Y. (1996). The mental representation of hand movements after parietal cortex damage. Science, 273(5281), 1564-1568.
171. Sommerlade, L., Henschel, K.,Wohlmuth, J., Jachan, M., Amtage, F., Hellwig, B., Schelter, B. (2009). Time-variant estimation of directed influences during Parkinsonian tremor. Journal of Physiology-Paris, 103(6), 348-352.
172. Song, L., Gordon, E., & Gysels, E. (2005). Phase synchrony rate for the recognition of motor imagery in brain-computer interface. In Y.Weiss, B. Schölkopf, & J. Platt (Eds.), Advances in neural information processing systems, 18. Cambridge, MA: MIT Press.
173. Spiegler,A., Graimann, B.,&Pfurtscheller, G. (2004). Phase coupling between different motor areas during tongue-movement imagery. Neuroscience Letters, 369(1), 50-54.
174. Srinivasan, R., Winter, W. R., Ding, J., & Nunez, P. L. (2007). EEG and MEG coherence:measures of functional connectivity at distinct spatial scales of neocortical dynamics. Journal of Neuroscience Methods, 166(1), 41-52.
175. Stam, C. J., Nolte, G., & Daffertshofer, A. (2007). Phase lag index: Assessment of functional connectivity from multi channel EEG and MEG with diminished bias from common sources. Human Brain Mapping, 28(11), 1178-1193.
176. Stancák, A., & Pfurtscheller, G. (1996). Event-related desynchronisation of central beta-rhythms during brisk and slow self-paced finger movements of dominant and nondominant hand. Cognitive Brain Research, 4(3), 171-183.
177. Stavrinou, M. L., Moraru, L., Cimponeriu, L., Della Penna, S., & Bezerianos, A. (2007). Evaluation of cortical connectivity during real and imagined rhythmic finger tapping. Brain Topography, 19(3), 137-145.
178. Stephan, K., & Frackowiak, R. (1996). Motor imagery-anatomical representation and electrophysiological characteristics. Neurochemical Research, 21(9), 1105-1116.
179. Suffczynski, P., Kalitzin, S., Pfurtscheller, G., & Da Silva, F. L. (2001). Computational model of thalamo-cortical networks: Dynamical control of alpha rhythms in relation to focal attention. International Journal of Psychophysiology, 43(1), 25-40.
180. Triesch, J., & von der Malsburg, C. (2001). Democratic integration: Self-organized integration of adaptive cues. Neural Computation, 13(9), 2049-2074.
181. Truccolo,W. A., Ding, M., Knuth, K. H., Nakamura, R., & Bressler, S. L. (2002). Trialto- trial variability of cortical evoked responses: Implications for the analysis of functional connectivity. Clinical Neurophysiology, 113(2), 206-226.
182. Tsiaras, V., Andreou, D., & Tollis, I. G. (2009). Brainnetvis: Analysis and visualization of brain functional networks. Paper presented at the Annual International Conference of the IEEE, Engineering in Medicine and Biology Society.
183. van Putten,M. J. (2003). Proposed link rates in the humanbrain. Journal of Neuroscience Methods, 127(1), 1-10.
184. Van Veen, B. D., Van Drongelen,W., Yuchtman,M., & Suzuki, A. (1997). Localization of brain electrical activity via linearly constrained minimum variance spatial filtering. IEEE Transactions on Biomedical Engineering, 44(9), 867-880.
185. Varela, F., Lachaux, J.-P., Rodriguez, E., & Martinerie, J. (2001). The brainweb: Phase synchronization and large-scale integration. Nature Reviews Neuroscience, 2(4), 229-239.
186. Velliste, M., Perel, S., Spalding, M. C., Whitford, A. S., & Schwartz, A. B. (2008). Cortical control of a prosthetic arm for self-feeding. Nature, 453(7198), 1098-1101.
187. Vicente, R.,Wibral, M.,Lindner,M.,&Pipa, G. (2011). Transfer entropy-amodel-free measure of effective connectivity for the neurosciences. Journal of Computational Neuroscience, 30(1), 45-67.
188. Vinck, M.,Oostenveld, R., vanWingerden,M., Battaglia, F., & Pennartz, C. M. (2011). An improved index of phase-synchronization for electrophysiological data in the presence of volume-conduction, noise and sample-size bias. NeuroImage, 55(4), 1548-1565.
189. Wang, D., Miao, D., & Blohm, G. (2012). Multi-class motor imagery EEG decoding for brain-computer interfaces. Frontiers in Neuroscience, 6.
190. Wang, S., & James, C. J. (2007). Extracting rhythmic brain activity for brain-computer interfacing through constrained independent component analysis. Computational Intelligence and Neuroscience, 2007, 1-9.
191. Wang, T., Deng, J., & He, B. (2004). Classifying EEG-based motor imagery tasks by means of time-frequency synthesized spatial patterns. Clinical Neurophysiology, 115(12), 2744-2753.
192. Wang, Y., Hong, B., Gao, X., & Gao, S. (2006). Phase synchrony measurement in motor cortex for classifying single-trial EEG during motor imagery. Paper presented at the 28th Annual International Conference of the IEEE, Engineering in Medicine and Biology Society.
193. Ward, D.-M., Jones, R. D., Bones, P. J., & Carroll, G. J. (1999). Enhancement of deep epileptiform activity in the EEG via 3-D adaptive spatial filtering. IEEE Transactions on Biomedical Engineering, 46(6), 707-716.
194. Wei, Q.,Wang, Y.,Gao, X.,&Gao, S. (2007). Amplitude and phase couplingmeasures for feature extraction in an EEG-based brain-computer interface. Journal of Neural Engineering, 4(2), 120.
195. Wolpaw, J. R., Birbaumer, N., McFarland, D. J., Pfurtscheller, G., & Vaughan, T. M. (2002). Brain-computer interfaces for communication and control. Clinical Neurophysiology, 113(6), 767-791.
196. Wolpaw, J. R., McFarland, D. J., & Vaughan, T. M. (2000). Brain-computer interface research at theWadsworth Center. IEEE Transactions on Rehabilitation Engineering, 8(2), 222-226.
197. Wriessnegger, S., Kurzmann, J., & Neuper, C. (2008). Spatio-temporal differences in brain oxygenation between movement execution and imagery: A multichannel near-infrared spectroscopy study. International Journal of Psychophysiology, 67(1), 54-63.
198. Wróbel, A. (2000). Beta activity: A carrier for visual attention. Acta neurobiologiae experimentalis, 60(2), 247-260.
199. Yuan, H., Doud, A., Gururajan, A., & He, B. (2008). Cortical imaging of eventrelated (de) synchronization during online control of brain-computer interface using minimum-norm estimates in frequency domain. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 16(5), 425-431.