BMI/BCI modeling and signal processing

Brain-Computer Interfaces: An International Assessment of Research and Development Trends

Volumn , Issue , 2008, Pages 47-64

  Principe, José C ^a   McFarland, Dennis J ^b  

^a UNIVERSITY OF FLORIDA   (United States)

^b WADSWORTH CENTER   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

EID: 84879450948     PISSN: None     EISSN: None     Source Type: Book    
DOI: 10.1007/978-1-4020-8705-9_4     Document Type: Chapter

References (74)

1. Arieli, A., D. Shoham, R. Hildesheim, and A. Grinvald. 1995. Coherent spatiotemporal patterns of ongoing activity revealed by realtime optical imaging coupled with single-unit recording in the cat visual cortex. J. Neurophysiol. 73:2072-2093.
2. Bashashati, A., S. Mason, R.K. Ward, and G.E. Birch. 2006. An improved asynchronous brain interface: making use of the temporal history of the LF-ASD feature vectors. J. Neural Eng. 3:87-94.
3. Berger, T.W., M. Baudry, R.D. Brinton, J.S. Liaw, V.Z. Marmarelis, A.Y. Park, B.J. Sheu, and A.R. Tanguay. 2001. Brain-implantable biomimetic electronics as the next era in neural prosthetics. Proc. IEEE 89:993-1012.
4. Blankertz, B., K.-R. Muller, D.J. Krusienski, G. Schalk, J.R. Wolpaw, A. Schlogl, G. Pfurtscheller, J. Millan, M. Schroder, and N. Birbaumer. 2006. The BCI competition III. Validating alternative approaches to actual BCI problems. IEEE Trans. Neural Syst. Rehab. Eng. 14:153-159.
5. Blankertz, B., K.-R. Muller, G. Curio, T.M. Vaughan, G. Schalk, J.R. Wolpaw, A. Schlogl, C. Neuper, G. Pfurtscheller, T. Hinterberger, M. Schroder, and N. Birbaumer. 2004. The BCI competition 2003: progress and perspectives in detection and discrimination of EEG single trials. IEEE Trans. Biomed. Eng. 51:1044-1051.
6. Boostani, R., and M.H. Moradi. 2004. A new approach in the BCI research based on fractal dimension as feature and Adaboost as classifier. J. Neural Eng. 1:212-217.
7. Brockwell, A.E., A.L. Rojas, and R.E. Kass. 2004. Recursive Bayesian decoding of motor cortical signals by particle filtering. J. Neurophysiol. 91:1899-1907.
8. Brown, E.N., D.P. Nguyen, L.M. Frank, M.A. Wilson, and V. Solo. 2001. An analysis of neural receptive field plasticity by point process adaptive filtering. PNAS 98:12261-12266.
9. Carmena, J.M., M.A. Lebedev, R.E. Crist, J.E. O'Doherty, D.M. Santucci, D.F. Dimitrov, P.G. Patil, C.S. Henriquez, and M.A. Nicolelis. 2003. Learning to control a brain-machine interface for reaching and grasping by primates. PLoS Biol. 1:1-16.
10. Chapin, J.K., K.A. Moxon, R.S. Markowitz, and M.A. Nicolelis. 1999. Real-time control of a robot arm using simultaneously recorded neurons in the motor cortex. Nat. Neurosci. 2(7):664-670.
11. Dayan, P. and L. Abbott. 2001. Theoretical neuroscience. Cambridge: MIT Press.
12. Donchin, E., K.M. Spencer, and R. Wijesinghe. 2000. The mental prosthesis: assessing the speed of a P300-based brain-computer interface. IEEE Trans. Neural Syst. Rehab. Eng. 8:174-179.
13. Donchin, O., A. Gribova, O. Steinberg, H. Bergman, S. Cardoso de Oliveira, and E. Vaadia. 2001. Local field potentials related to bimanual movements in the primary and supplementary motor cortices. Exp. Brain Res. 140:46-55.
14. Donoghue, J.P., J.N. Sanes, N.G. Hatsopoulos, and G. Gaal. 1998. Neural discharge and local field potential oscillations in primate motor cortex during voluntary movements. J. Neurophysiol. 79:159-173.
15. Eden, U.T., L.M. Frank, R. Barbieri, V. Solo, and E.B. Brown. 2004. Dynamic analysis of neural encoding by point process adaptive filtering. Neural Comput. 16(5):971-998.
16. Fabiani, G.E., D.J. McFarland, J.R. Wolpaw, and G. Pfurtscheller. 2004. Conversion of EEG activity into cursor movement by a brain computer interface. IEEE Trans. Neural Syst. Rehab. Eng. 12:331-338.
17. Fee M.S., P.P. Mitra, and D. Kleinfeld. 1996. Automatic sorting of multiple-unit neuronal signals in the presence of anisotropic and non-Gaussian variability. J. Neurosci. Meth. 69:175-188.
18. Freeman, W.J. 2004a. Origin, structure, and role of background EEG activity. Part 1. Analytic amplitude. Clin. Neurophysiol. 115:2077-2088.
19. Freeman, W.J. 2004b. Origin, structure, and role of background EEG activity. Part 2. Analytic phase. Clin. Neurophysiol. 115:2089-2107.
20. Freeman, W.J. 2005. Origin, structure, and role of background EEG activity. Part 3. Neural frame classification. Clin. Neurophysiol. 116(5):1118-1129.
21. Freeman, W.J. 2006. Origin, structure, and role of background EEG activity. Part 4. Neural frame simulation. Clin. Neurophysiol. 117(3):572-589.
22. Gao, Y., M.J. Black, E. Bienenstock, W. Wu, and J.P. Donoghue. 2003. A quantitative comparison of linear and nonlinear models of motor cortical activity for the encoding and decoding of arm motions. The First International IEEE EMBS Conference on Neural Engineering. Capri, Italy.
23. Georgopoulos, A.P., A.B. Schwartz, and R.E. Kettner. 1986. Neuronal population coding of movement direction. Science 233(4771):1416-1419.
24. Gunduz, A., J. Principe, and W.J. Freeman. 2005. Online detection of motor signatures in multichannel ECoG. In Proceedings, 27th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, IEEE-EMBS. 3683-3686.
25. Haykin, S. 2002. Adaptive filter theory. Englewood Cliffs, NJ: Prentice-Hall.
26. Hochberg, L.R., M.D. Serruya, G.M. Friehs, J.A. Mukand, M. Saleh, A.H. Caplan, A. Branner, D. Chen, R.D. Penn, and J.P. Donoghue. 2006. Neuronal ensemble control of prosthetic devices by a human with tetraplegia. Nature 442:164-171.
27. Kim P., J. Sanchez, Y. Rao, D. Erdogmus, J. Carmena, M. Lebedev, M. Nicolelis, and J. Principe. 2006. A comparison of optimal MIMO linear and nonlinear models for brain-machine interfaces. J. Neural Eng. 3(2):145-161.
28. Kim S., J. Sanchez, D. Erdogmus, Y. Rao, J. Principe, and M. Nicolelis. 2003. Divide-and-conquer approach for brain machine interfaces: nonlinear mixture of competitive linear models. Neural Networks 16:865-871.
29. Krusienski, D.J., G. Schalk, D.J. McFarland, and J.R. Wolpaw. 2007. A mu-rhythm matched filter for continuous control of a brain-computer interface. IEEE Trans. Biomed. Eng. 54(2):273-280.
30. Lemm, S., B. Blankertz, G. Curio, and R.-K. Muller. 2005. Spatio-spectral filters for improving the classification of single trial EEG. IEEE Trans. Biomed. Eng. 52:1541-1548.
31. Leuthardt, E.C., G. Schalk, J.R. Wolpaw, J.G. Ojemann, and D.W. Moran. 2004. A brain-computer interface using electrocorticographic signals in humans. J. Neural Eng. 1:63-71.
32. Lewicki, M.S. 1998. A review of methods for spike sorting: the detection and classification of neural action potentials. Network Comput. Neural Syst. 9:R53-R78.
33. Li, Y., X. Gao, H. Liu, and S. Gao. 2004. Classification of single-trial encephalogram during finger movement. IEEE Trans. Biomed. Eng. 51:1019-1025.
34. Marmarelis, P.Z. and V.Z. Marmarelis. 1978. Analysis of physiological systems: the white noise approach. New York: Plenum.
35. Marmarelis, V. 1993. Identification of nonlinear biological systems using Laguerre expansions of kernels. J. Biomed. Eng. 21:573-589.
36. Mason, S.G. and G.E. Birch. 2000. A brain-controlled switch for asynchronous control applications. IEEE Trans. Biomed. Eng. 47:1297-1307.
37. McFarland, D.J., C.W. Anderson, K.-R. Muller, A. Schlogl, and D.J. Krusienski. 2006. BCI meeting 2005-workshop on BCI signal processing: feature extraction and translation. IEEE Trans. Neural Syst. Rehab. Eng. 14:135-138.
38. McFarland, D.J. and J.R. Wolpaw. 2005. Sensorimotor rhythm-based brain-computer interface (BCI): feature selection by regression improves performance. IEEE Trans. Neural Syst. Rehab. Eng. 13:372-379.
39. McFarland, D.J., L.M. McCane, S.V. David, and J.R. Wolpaw. 1997. Spatial filter selection for an EEG-based brain-computer interface. Electroencephalogr. Clin. Neurophysiol. 103:386-394.
40. Mehring, C., J. Rickert, E. Vaadia, S. Oliveira, A. Aertsen, and S. Rotter. 2003. Inference of hand movements from LFPs in monkey motor cortex. Nat. NeuroSci. 6(12):1253-1254.
41. Millan, J.R., F. Renkens, J. Mourino, and W. Gerstner. 2002. Noninvasive brain-actuated control of a mobile robot by human EEG. IEEE Trans. Biomed. Eng. 51:1026-1033.
42. Moran, D.W. and A.B. Schwartz. 1999. Motor cortical representation of speed and direction during reaching. J. Neurophysiol. 82(5):2676-2692.
43. Naeem, M., C. Brunner, R. Leeb, B. Graimann, and G. Pfurtscheller. 2006. Seperability of four-class motor imagery data using independent components analysis. J. Neural Eng. 3:208-216.
44. Nicolelis, M. 2001. Action from thoughts. Nature 409:403-407.
45. Paninski, L., S. Shoham, M.R. Fellows, N.G. Hatsopoulos, and J.P. Donoghue. 2004. Superlinear population encoding of dynamic hand trajectory in primary motor cortex. J. Neurosci. 24(39):8551-8561.
46. Peters, B.O., G. Pfurtscheller, and H. Flybjerg. 1998. Mining multi-channel EEG for its information content: an ANN-based method for a brain-computer interface. Neural Networks 11:1429-1433.
47. Pfurtscheller, G., G.R. Muller-Putz, A. Schlogl, B. Graimann, R. Scherer, R. Leeb, C. Brunner, C. Keinrath, F. Lee, G. Townsend, C. Vidaurre, and C. Neuper. 2006. Fifteen years of BCI research at Graz University of Technology: current projects. IEEE Trans. Neural Syst. Rehab. Eng. 14:205-210.
48. Rezaei, S., K. Tavakolian, A.M. Nasrabadi, and S.K. Setarehdan. 2006. Different classification techniques considering brain-computer interface applications. J. Neural Eng. 3:139-144.
49. Rickert, J., S. Cardoso de Oliveira, E. Vaadia, A. Aertsen, S. Rotter, and C. Mehring. 2005. Encoding of movement direction in different frequency ranges of motor cortical local field potentials. J. Neurosci. 25:8815-8824.
50. Sajda, P., A. Gerson, K.-R. Muller, B. Blankertz, and L. Parra. 2003. A data analysis competition to evaluate machine learning algorithms for use in brain-computer interfaces. IEEE Trans. Neural Syst. Rehab. Eng. 11:184-185.
51. Sanchez J., J. Carmena, M. Lebedev, M. Nicolelis, J. Harris, and J. Principe. 2003. Ascertaining the importance of neurons to develop better brain machine interfaces. IEEE Trans. Biomed. Eng. 61:943-953.
52. Sanchez J., D. Erdogmus, J. Principe, J. Wessberg, and M. Nicolelis. 2002a. A comparison between nonlinear mappings and linear state estimation to model the relation from motor cortical neuronal firing to hand movements. Workshop on Motor Control, Edinburg, Scotland, 59-65.
53. Sanchez, J.C., P.R. Carney, and J.C. Principe. 2006. Analysis of amplitude modulated control features for ECoG neuroprosthetics. Twentyeighth Annual International Conference of the IEEE Engineering in Medicine and Biology Society, IEEE-EMBS. 5468-5471.
54. Sanchez, J.C., S.P. Kim, D. Erdogmus, Y.N. Rao, J.C. Principe, J. Wessberg, and M. Nicolelis. 2002b. Input-output mapping performance of linear and nonlinear models for estimating hand trajectories from cortical neuronal firing patterns. International Workshop on Neural Networks for Signal Processing. Martigny, Switzerland: IEEE.
55. Schlogl, A., F. Lee, H. Bischof, and G. Pfurtscheller. 2005. Characterization of four-class motor imagery data for the BCI-competition 2005. J. Neural Eng. 2:14-22.
56. Schwartz, A., D. Taylor, and S. Tillery. 2001. Extraction algorithms for cortical control of arm prosthetics. Curr. Opin. Neurobiol. 11:701-708.
57. Serby, H., E. Yom-Tov, and G.F. Inbar. 2005. An improved P300-based brain-computer interface. IEEE Trans. Neural Syst. Rehab. Eng. 13:89-98.
58. Serruya, M.D., N.G. Hatsopoulos, L. Paninski, M.R. Fellows, and J.P. Donoghue. 2002. Brain-machine interface: instant neural control of a movement signal. Nature 416:141-142.
59. Shenoy, K.V., D. Meeker, S. Cao, S.A. Kureshi, B. Pesaran, C.A. Buneo, A.P. Batista, P.P. Mitra, J.W. Burdick, and R.A. Andersen. 2003. Neural prosthetic control signals from plan activity. NeuroReport 14:591-597.
60. Shenoy, P., M. Krauledat, B. Blankertz, R.P.N. Rao, and R.-K. Muller. 2006. Towards adaptive classification for BCI. J. Neural Eng. 3:13-23.
61. Simoncelli, E.P., L. Paninski, J. Pillow, and O. Schwartz. 2004. Characterization of neural responses with stochastic stimuli. In: The New Cognitive Neuroscience, 3rd ed. Ed. Gazzaniga, M. Cambridge: MIT Press.
62. Soderstrom, T. and P. Stoica. 1989. System identification. Englewood Cliffs, NJ: Prentice Hall.
63. Song, D., V.Z. Marmarelis, and T.W. Berger. 2002. Parametric and nonparametric models of short-term plasticity. Proceedings of the Second Joint EMBS/BMES Conference 3:1964-1965.
64. Taylor, D.M., S.I.H. Tillery, and A.B. Schwartz. 2002. Direct cortical control of 3D neuroprosthetic devices. Science 296(5574):1829-1832.
65. Tillery, S.I.H., D.M. Taylor, and A.B. Schwartz. 2003. Training in cortical control of neuroprosthetic devices improves signal extraction from small neuronal ensembles. Rev. NeuroSci. 14(1-2):107-119.
66. Vidaurre, C., A. Schlogl, R. Cabeza, R. Scherer, and G. Pfurtscheller. 2005. Adaptive on-line classification for EEG-based brain computer interfaces with AAR parameters and band power estimates. Biomed. Technik 50:350-354.
67. Wang, T., J. Deng, and B. He. 2004. Classifying EEG-based motor imagery tasks by means of time-frequency synthesized spatial patterns. Clin. Neurophysiol. 115:2744-2753.
68. Wang, Y., A.R.C. Paiva, and J.C. Principe. 2006. A Monte Carlo sequential estimation for point process optimum filtering. Proc. IJCNN 2006, Portland.
69. Wessberg, J., C.R. Stambaugh, J.D. Kralik, P.D. Beck, M. Laubach, J.K. Chapin, J. Kim, S.J. Biggs, M.A. Srinivasan, and A. Nicolelis. 2000. Real-time prediction of hand trajectory by ensembles of cortical neurons in primates. Nature 408(6810):361-365.
70. Wolpaw, J.R., N. Birbaumer, D.J. McFarland, G. Pfurtscheller, and T.M. Vaughan. 2002. Brain-computer interfaces for communication and control. Clin. Neurophysiol. 113:767-791.
71. Wood, F., M.J. Black, C. Vargas-Irwin, M. Fellows, J.P. Donoghue. 2004. IEEE Trans. Biomed. Eng. 51(6):912-918.
72. Wu, W., M.J. Black, Y. Gao, E. Bienenstock, M. Serruya, and J.P. Donoghue. 2002. Inferring hand motion from multicell recordings in motor cortex using a Kalman filter. SAB Workshop on Motor Control in Humans and Robots: on the Interplay of Real Brains and Artificial Devices. University of Edinburgh, Scotland.
73. Wu, W., Y. Gao, E. Bienenstock, J.P. Donoghue, and M.J. Black. 2005. Neural Comput. 18:80-118.
74. Yom-Tov, E. and G.F. Inbar. 2003. Detection of movement-related potentials from the electroencephalogram for possible use in a brain-computer interface. Med. Biol. Eng. Comput. 41:85-93.