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Computational Intelligence and Neuroscience
Volume 2009 (2009), Article ID 656092, 12 pages
http://dx.doi.org/10.1155/2009/656092
Review Article

EEG/MEG Source Imaging: Methods, Challenges, and Open Issues

1Department of Biomedical Engineering, Tampere University of Technology, 33101 Tampere, Finland
2Department of Electrical and Electronics Engineering, Middle East Technical University, 06531 Ankara, Turkey
3SCD, Department of Electrical Engineering (ESAT), Katholieke Universiteit Leuven, Kasteelpark Arenberg 10, B-3001 Leuven, Belgium
4MOBILAB, Biosciences and Technology Department, Katholieke Hogeschool Kempen, Kleinhoefstraat 4, B-2440 Geel, Belgium
5Department of Biomedical Physics, University of Warsaw ul. Hoża 69, 00-681 Warszawa, Poland
6Faculty of Electrical Engineering and Computing, University of Zagreb, 10000 Zagreb, Croatia
7Department of Physics, Faculty of Science, University of Zagreb, 10000 Zagreb, Croatia
8National Institute for Laser, Plasma and Radiation Physics, Laser Department, 077125 Bucharest, Romania
9Department of Medical Physics, School of Medicine, University of Patras, 26504 Patras, Greece
10Electrical Neuroimaging Group, Department of Clinical Neurosciences, Geneva University Hospital, 1211 Geneva, Switzerland
11Neurodynamics Laboratory, Department of Psychiatry and Clinical Psychobiology, University of Barcelona, 08035 Barcelona, Catalonia, Spain

Received 25 November 2008; Revised 31 March 2009; Accepted 29 April 2009

Academic Editor: Laura Astolfi

Copyright © 2009 Katrina Wendel et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Linked References

  1. D. Venes, Taber's Cyclopedic Medical Dictionary, F. A. Davis Company, Philadelphia, Pa, USA, 20th edition, 2005.
  2. A. M. Dale and M. I. Sereno, “Improved localization of cortical activity by combining EEG and MEG with MRI cortical surface reconstruction: a linear approach,” Journal of Cognitive Neuroscience, vol. 5, no. 2, pp. 162–176, 1993. View at Google Scholar
  3. R. Grave de Peralta Menendez, S. L. Gonzalez Andino, and B. Lutkenhoner, “Figures of merit to compare distributed linear inverse solutions,” Brain Topography, vol. 9, no. 2, pp. 117–124, 1996. View at Publisher · View at Google Scholar
  4. A. J. R. Leal, A. I. Dias, and J. P. Vieira, “Analysis of the EEG dynamics of epileptic activity in gelastic seizures using decomposition in independent components,” Clinical Neurophysiology, vol. 117, no. 7, pp. 1595–1601, 2006. View at Publisher · View at Google Scholar
  5. P. Anderer, G. Kloesch, G. Gruber et al., “Low-resolution brain electromagnetic tomography revealed simultaneously active frontal and parietal sleep spindle sources in the human cortex,” Neuroscience, vol. 103, no. 3, pp. 581–592, 2001. View at Publisher · View at Google Scholar
  6. P. Durka, A. Matysiak, E. M. Montes, P. V. Sosa, and K. J. Blinowska, “Multichannel matching pursuit and EEG inverse solutions,” Journal of Neuroscience Methods, vol. 148, no. 1, pp. 49–59, 2005. View at Publisher · View at Google Scholar
  7. C. E. Davila and R. Srebro, “Subspace averaging of steady-state visual evoked potentials,” IEEE Transactions on Biomedical Engineering, vol. 47, no. 6, pp. 720–728, 2000. View at Google Scholar
  8. O. Bai, M. Nakamura, T. Nagamine, and H. Shibasaki, “Parametric modeling of somatosensory evoked potentials using discrete cosine transform,” IEEE Transactions on Biomedical Engineering, vol. 48, no. 11, pp. 1347–1351, 2001. View at Publisher · View at Google Scholar
  9. T. W. Picton, Handbook of Electroencephalography and Clinical Neurophysiology: Human Event-Related Potentials, Elsevier, Amsterdam, The Netherlands, 1988.
  10. B. H. Jansen, G. Agarwal, A. Hegde, and N. N. Boutros, “Phase synchronization of the ongoing EEG and auditory EP generation,” Clinical Neurophysiology, vol. 114, no. 1, pp. 79–85, 2003. View at Publisher · View at Google Scholar
  11. S. Rush and D. A. Driscoll, “EEG electrode sensitivity—an application of reciprocity,” IEEE Transactions on Biomedical Engineering, vol. 16, no. 1, pp. 15–22, 1969. View at Google Scholar
  12. B. J. Roth, M. Balish, A. Gorbach, and S. Sato, “How well does a three-sphere model predict positions of dipoles in a realistically shaped head?” Electroencephalography and Clinical Neurophysiology, vol. 87, no. 4, pp. 175–184, 1993. View at Publisher · View at Google Scholar
  13. A. Crouzeix, B. Yvert, O. Bertrand, and J. Pernier, “An evaluation of dipole reconstruction accuracy with spherical and realistic head models in MEG,” Clinical Neurophysiology, vol. 110, no. 12, pp. 2176–2188, 1999. View at Publisher · View at Google Scholar
  14. B. N. Cuffin, “EEG localization accuracy improvements using realistically shaped head models,” IEEE Transactions on Biomedical Engineering, vol. 43, no. 3, pp. 299–303, 1996. View at Publisher · View at Google Scholar
  15. M. S. Hamalainen and J. Sarvas, “Realistic conductivity geometry model of the human head for interpretation of neuromagnetic data,” IEEE Transactions on Biomedical Engineering, vol. 36, no. 2, pp. 165–171, 1989. View at Publisher · View at Google Scholar
  16. K. Wendel, N. G. Narra, M. Hannula, P. Kauppinen, and J. Malmivuo, “The influence of CSF on EEG sensitivity distributions of multilayered head models,” IEEE Transactions on Biomedical Engineering, vol. 55, no. 4, pp. 1454–1456, 2008. View at Publisher · View at Google Scholar
  17. K. Wendel, M. Osadebey, and J. Malmivuo, “Incorporating anthropometric and craniometric data into realistically-shaped volume conductor head models,” in Proceedings of the 11th World Congress on Medical Physics and Biomedical Engineering, Munich, Germany, September 2009.
  18. J. Haueisen, C. Ramon, M. Eiselt, H. Brauer, and H. Nowak, “Influence of tissue resistivities on neuromagnetic fields and electric potentials studied with a finite element model of the head,” IEEE Transactions on Biomedical Engineering, vol. 44, no. 8, pp. 727–735, 1997. View at Publisher · View at Google Scholar
  19. H. P. Schwan, “Determination of biological impedances,” in Physical Techniques in Biological Research, W. L. Nastuk, Ed., Academic Press, New York, NY, USA, 1963. View at Google Scholar
  20. C. Gabriel, S. Gabriel, and E. Corthout, “The dielectric properties of biological tissues—I: literature survey,” Physics in Medicine and Biology, vol. 41, no. 11, pp. 2231–2249, 1996. View at Publisher · View at Google Scholar
  21. L. A. Geddes and L. E. Baker, “The specific resistance of biological material—a compendium of data for the biomedical engineer and physiologist,” Medical & Biological Engineering, vol. 5, no. 3, pp. 271–293, 1967. View at Publisher · View at Google Scholar
  22. B. M. Radich and K. M. Buckley, “EEG dipole localization bounds and MAP algorithms for head models with parameter uncertainties,” IEEE Transactions on Biomedical Engineering, vol. 42, no. 3, pp. 233–241, 1995. View at Publisher · View at Google Scholar
  23. S. P. van den Broek, F. Reinders, M. Donderwinkel, and M. J. Peters, “Volume conduction effects in EEG and MEG,” Electroencephalography and Clinical Neurophysiology, vol. 106, no. 6, pp. 522–534, 1998. View at Publisher · View at Google Scholar
  24. G. Huiskamp, M. Vroeijenstijn, R. van Dijk, G. Wieneke, and A. C. van Huffelen, “The need for correct realistic geometry in the inverse EEG problem,” IEEE Transactions on Biomedical Engineering, vol. 46, no. 11, pp. 1281–1287, 1999. View at Publisher · View at Google Scholar
  25. B. Vanrumste, G. Van Hoey, R. Van de Walle, M. D'Havé, I. Lemahieu, and P. Boon, “Dipole location errors in electroencephalogram source analyssis due to volume conductor model errors,” Medical and Biological Engineering and Computing, vol. 38, no. 5, pp. 528–534, 2000. View at Google Scholar
  26. N. G. Gencer and C. E. Acar, “Sensitivity of EEG and MEG measurements to tissue conductivity,” Physics in Medicine and Biology, vol. 49, no. 5, pp. 701–717, 2004. View at Publisher · View at Google Scholar
  27. J. O. Ollikainen, M. Vauhkonen, P. A. Karjalainen, and J. P. Kaipio, “Effects of local skull inhomogeneities on EEG source estimation,” Medical Engineering and Physics, vol. 21, no. 3, pp. 143–154, 1999. View at Publisher · View at Google Scholar
  28. R. Hoekema, G. H. Wieneke, F. S. S. Leijten et al., “Measurement of the conductivity of skull, temporarily removed during epilepsy surgery,” Brain Topography, vol. 16, no. 1, pp. 29–38, 2003. View at Publisher · View at Google Scholar
  29. K. Wendel and J. Malmivuo, “Correlation between live and post mortem skull conductivity measurements,” Proceedings of Annual International Conference of the IEEE Engineering in Medicine and Biology Society, vol. 1, pp. 4285–4288, 2006. View at Google Scholar
  30. C. M. Michel, G. Lantz, L. Spinelli, R. Grave de Peralta Menendez, T. Landis, and M. Seeck, “128-channel EEG source imaging in epilepsy: clinical yield and localization precision,” Journal of Clinical Neurophysiology, vol. 21, no. 2, pp. 71–83, 2004. View at Publisher · View at Google Scholar
  31. S. L. Gonzalez Andino, R. Grave de Peralta Menendez, A. Khateb, T. Landis, and A. J. Pegna, “Electrophysiological correlates of affective blindsight,” NeuroImage, vol. 44, no. 2, pp. 581–589, 2009. View at Publisher · View at Google Scholar
  32. K. L. Moore and A. F. Dalley, Clinically Oriented Anatomy, Lippincott Williams & Wilkins, Philadelphia, Pa, USA, 5th edition, 2005.
  33. T. F. Oostendorp, J. Delbeke, and D. F. Stegeman, “The conductivity of the human skull: results of in vivo and in vitro measurements,” IEEE Transactions on Biomedical Engineering, vol. 47, no. 11, pp. 1487–1492, 2000. View at Google Scholar
  34. J. D. Kosterich, K. R. Foster, and S. R. Pollack, “Dielectric permittivity and electrical conductivity of fluid saturated bone,” IEEE Transactions on Biomedical Engineering, vol. 30, no. 2, pp. 81–86, 1983. View at Google Scholar
  35. J. D. Kosterich, K. R. Foster, and S. R. Pollack, “Dielectric properties of fluid-saturated bone. The effect of variation in conductivity of immersion fluid,” IEEE Transactions on Biomedical Engineering, vol. 31, no. 4, pp. 369–374, 1984. View at Google Scholar
  36. S. I. Goncalves, J. C. de Munck, J. P. A. Verbunt, F. Bijma, R. M. Heethaar, and F. L. da Silva, “In vivo measurement of the brain and skull resistivities using an EIT-based method and realistic models for the head,” IEEE Transactions on Biomedical Engineering, vol. 50, no. 6, pp. 754–767, 2003. View at Publisher · View at Google Scholar
  37. H. Griffiths, W. R. Stewart, and W. Cough, “Magnetic induction tomography. A measuring system for biological tissues,” Annals of the New York Academy of Sciences, vol. 873, pp. 335–345, 1999. View at Publisher · View at Google Scholar
  38. N. G. Gencer and M. N. Tek, “Electrical conductivity imaging via contactless measurements,” IEEE Transactions on Medical Imaging, vol. 18, no. 7, pp. 617–627, 1999. View at Google Scholar
  39. N. Zhang, Electrical Impedance Tomography Based on Current Density Imaging, University of Toronto, Toronto, Canada, 1992.
  40. Y. Z. Ider, S. Onart, and W. R. B. Lionheart, “Uniqueness and reconstruction in magnetic resonance-electrical impedance tomography (MR-EIT),” Physiological Measurement, vol. 24, no. 2, pp. 591–604, 2003. View at Publisher · View at Google Scholar
  41. A. Gevins, H. Leong, M. E. Smith, J. Le, and R. Du, “Mapping cognitive brain function with modern high-resolution electroencephalography,” Trends in Neurosciences, vol. 18, no. 10, pp. 429–436, 1995. View at Publisher · View at Google Scholar
  42. A. Gevins, J. Le, H. Leong, L. K. McEvoy, and M. E. Smith, “Deblurring,” Journal of Clinical Neurophysiology, vol. 16, no. 3, pp. 204–213, 1999. View at Publisher · View at Google Scholar
  43. F. Babiloni, F. Cincotti, F. Carducci, P. M. Rossini, and C. Babiloni, “Spatial enhancement of EEG data by surface Laplacian estimation: the use of magnetic resonance imaging-based head models,” Clinical Neurophysiology, vol. 112, no. 5, pp. 724–727, 2001. View at Publisher · View at Google Scholar
  44. C. M. Michel, M. M. Murray, G. Lantz, S. Gonzalez, L. Spinelli, and R. Grave de Peralta Menendez, “EEG source imaging,” Clinical Neurophysiology, vol. 115, no. 10, pp. 2195–2222, 2004. View at Publisher · View at Google Scholar
  45. A. Gevins, P. Brickett, B. Costales, J. Le, and B. Reutter, “Beyond topographic mapping: towards functional-anatomical imaging with 124-channel EEGs and 3-D MRIs,” Brain Topography, vol. 3, no. 1, pp. 53–64, 1990. View at Google Scholar
  46. R. Srinivasan, P. L. Nunez, D. M. Tucker, R. B. Silberstein, and P. J. Cadusch, “Spatial sampling and filtering of EEG with spline laplacians to estimate cortical potentials,” Brain Topography, vol. 8, no. 4, pp. 355–366, 1996. View at Google Scholar
  47. F. Babiloni, C. Babiloni, F. Carducci et al., “High resolution EEG: a new model-dependent spatial deblurring method using a realistically-shaped MR-constructed subject's head model,” Electroencephalography and Clinical Neurophysiology, vol. 102, no. 2, pp. 69–80, 1997. View at Publisher · View at Google Scholar
  48. O. R. M. Ryynanen, J. A. K. Hyttinen, and J. Malmivuo, “Effect of measurement noise and electrode density on the spatial resolution of cortical potential distribution with different resistivity values for the skull,” IEEE Transactions on Biomedical Engineering, vol. 53, no. 9, pp. 1851–1858, 2006. View at Publisher · View at Google Scholar
  49. R. Srinivasan, D. M. Tucker, and M. Murias, “Estimating the spatial Nyquist of the human EEG,” Behavior Research Methods, Instruments, & Computers, vol. 30, no. 1, pp. 8–19, 1998. View at Google Scholar
  50. Y. Wang and B. He, “A computer simulation study of cortical imaging from scalp potentials,” IEEE Transactions on Biomedical Engineering, vol. 45, no. 6, pp. 724–735, 1998. View at Publisher · View at Google Scholar
  51. A. Berman and R. Plemmons, Nonnegative Matrices in the Mathematical Sciences, SIAM, Philadelphia, Pa, USA, 1994.
  52. W. H. Press, S. A. Teukolsky, W. T. Vetterling, and B. P. Flannery, Numerical Recipes in C, Cambridge University Press, Cambridge, UK, 1995.
  53. J. W. Ruge and K. Stüben, “Algebraic multigrid (AMG),” in Multigrid Methods, S. F. McCormick, Ed., vol. 3 of Frontiers in Applied Mathematics, pp. 73–130, SIAM, Philadelphia, Pa, USA, 1987. View at Google Scholar
  54. W. L. Briggs, V. E. Henson, and S. F. McCormick, A Multigrid Tutorial, SIAM, Philadelphia, Pa, USA, 2000.
  55. A. C. L. Barnard, I. M. Duck, and M. S. Lynn, “The application of electromagnetic theory to electrocardiography—I: derivation of the integral equations,” Biophysics Journal, vol. 7, pp. 443–462, 1967. View at Google Scholar
  56. Z. Akalin-Acar and N. G. Gencer, “An advanced boundary element method (BEM) implementation for the forward problem of electromagnetic source imaging,” Physics in Medicine and Biology, vol. 49, no. 21, pp. 5011–5028, 2004. View at Publisher · View at Google Scholar
  57. Y. Ataseven, Z. Akalin-Acar, C. E. Acar, and N. G. Gencer, “Parallel implementation of the accelerated BEM approach for EMSI of the human brain,” Medical and Biological Engineering and Computing, vol. 46, no. 7, pp. 671–679, 2008. View at Publisher · View at Google Scholar
  58. S. L. Gonzalez Andino, R. Grave de Peralta Menendez, R. Biscay, J. C. Jimenez, R. D. Pascual, and J. Lemagne, “Projective methods for the magnetic direct problem,” in Advances in Biomagnetism, New York, NY, USA, 1989.
  59. B. N. Datta, Numerical Linear Algebra and Applications, Brooks/Cole, Pacific Grove, Calif, USA, 1995.
  60. W. L. Briggs, A Multigrid Tutorial, SIAM, Philadelphia, Pa, USA, 1987.
  61. R. Hoekema, K. Venner, J. J. Struijk, and J. Holsheimer, “Multigrid solution of the potential field in modeling electrical nerve stimulation,” Computers and Biomedical Research, vol. 31, no. 5, pp. 348–362, 1998. View at Publisher · View at Google Scholar
  62. P. Laarne, J. Hyttinen, S. Dodel, J. Malmivuo, and H. Eskola, “Accuracy of two dipolar inverse algorithms applying reciprocity for forward calculation,” Computers and Biomedical Research, vol. 33, no. 3, pp. 172–185, 2000. View at Publisher · View at Google Scholar
  63. Y. Saad, Iterative Methods for Sparse Linear Systems, SIAM, Philadelphia, Pa, USA, 2nd edition, 2003.
  64. L. A. Neilson, M. Kovalyov, and Z. J. Koles, “A computationally efficient method for accurately solving the EEG forward problem in a finely discretized head model,” Clinical Neurophysiology, vol. 116, no. 10, pp. 2302–2314, 2005. View at Publisher · View at Google Scholar
  65. J. F. Thompson, B. K. Soni, and N. P. Weatherrill, Handbook of Grid Generation, CRC Press, Boca Raton, Fla, USA, 1998.
  66. N. Ottosen and H. Peterson, Introduction to the Finite Element Method, Prentice-Hall, Englewood Cliffs, NJ, USA, 1992.
  67. H. I. Saleheen and T. Kwong, “New finite difference formulations for general inhomogeneous anisotropic bioelectric problems,” IEEE Transactions on Biomedical Engineering, vol. 44, no. 9, pp. 800–809, 1997. View at Publisher · View at Google Scholar
  68. R. Plonsey, “The nature of sources of bioelectric and biomagnetic fields,” Biophysical Journal, vol. 39, no. 3, pp. 309–312, 1982. View at Google Scholar
  69. M. Hamalainen, R. J. Ilmoniemi, and J. Sarvas, “Interdependence of information conveyed by the magnetoencephalogram and the electroencephalogram,” in Theory and Applications of Inverse Problems, H. Hario, Ed., John Wiley & Sons, New York, NY, USA, 1988. View at Google Scholar
  70. J. Malmivuo, V. Suihko, and H. Eskola, “Sensitivity distributions of EEG and MEG measurements,” IEEE Transactions on Biomedical Engineering, vol. 44, no. 3, pp. 196–208, 1997. View at Publisher · View at Google Scholar
  71. J. Malmivuo and V. E. Suihko, “Effect of skull resistivity on the spatial resolutions of EEG and MEG,” IEEE Transactions on Biomedical Engineering, vol. 51, no. 7, pp. 1276–1280, 2004. View at Publisher · View at Google Scholar
  72. A. K. Liu, A. M. Dale, and J. W. Belliveau, “Monte Carlo simulation studies of EEG and MEG localization accuracy,” Human Brain Mapping, vol. 16, no. 1, pp. 47–62, 2002. View at Publisher · View at Google Scholar
  73. S. Waldert, H. Preissl, E. Demandt et al., “Hand movement direction decoded from MEG and EEG,” Journal of Neuroscience, vol. 28, no. 4, pp. 1000–1008, 2008. View at Publisher · View at Google Scholar
  74. M. Hamalainen, R. Hari, R. J. Ilmoniemi, J. Knuutila, and O. V. Lounasmaa, “Magnetoencephalography—theory, instrumentation, and applications to noninvasive studies of the working human brain,” Reviews of Modern Physics, vol. 65, no. 2, pp. 413–497, 1993. View at Publisher · View at Google Scholar
  75. R. Grave de Peralta Menendez and S. L. Gonzalez-Andino, “A critical analysis of linear inverse solutions to the neuroelectromagnetic inverse problem,” IEEE Transactions on Biomedical Engineering, vol. 45, no. 4, pp. 440–448, 1998. View at Google Scholar
  76. S. Supek and C. J. Aine, “Simulation studies of multiple dipole neuromagnetic source localization: model order and limits of source resolution,” IEEE Transactions on Biomedical Engineering, vol. 40, no. 6, pp. 529–540, 1993. View at Publisher · View at Google Scholar
  77. S. Supek and C. J. Aine, “Spatio-temporal modeling of neuromagnetic data—I: multi-source location versus time-course estimation accuracy,” Human Brain Mapping, vol. 5, no. 3, pp. 139–153, 1997. View at Publisher · View at Google Scholar
  78. M. S. Hamalainen and R. J. Ilmoniemi, “Interpreting magnetic fields of the brain: minimum norm estimates,” Medical and Biological Engineering and Computing, vol. 32, no. 1, pp. 35–42, 1994. View at Google Scholar
  79. O. Hauk, “Keep it simple: a case for using classical minimum norm estimation in the analysis of EEG and MEG data,” NeuroImage, vol. 21, no. 4, pp. 1612–1621, 2004. View at Publisher · View at Google Scholar
  80. M. Scherg and D. Von Cramon, “Evoked dipole source potentials of the human auditory cortex,” Electroencephalography and Clinical Neurophysiology, vol. 65, no. 5, pp. 344–360, 1986. View at Google Scholar
  81. M. Scherg and T. W. Picton, “Separation and identification of event-related potential components by brain electric source analysis,” Electroencephalography and Clinical Neurophysiology. Supplement, vol. 42, pp. 24–37, 1991. View at Google Scholar
  82. F. Babiloni, C. Babiloni, L. Locche, F. Cincotti, P. M. Rossini, and F. Carducci, “High-resolution electro-encephalogram: source estimates of Laplacian-transformed somatosensory-evoked potentials using a realistic subject head model constructed from magnetic resonance images,” Medical and Biological Engineering and Computing, vol. 38, no. 5, pp. 512–519, 2000. View at Google Scholar
  83. J. Capon, “High resolution frequency-wavenumber,” Proceedings of the IEEE, vol. 57, no. 8, pp. 1408–1418, 1969. View at Google Scholar
  84. G. E. Backus and J. F. Gilbert, “The resolving power of gross earth data,” Geophysical Journal of the Royal Astronomical Society, vol. 16, pp. 169–205, 1968. View at Google Scholar
  85. K. Sekihara, S. S. Nagarajan, D. Poeppel, and A. Marantz, “Performance of an MEG adaptive-beamformer technique in the presence of correlated neural activities: effects on signal intensity and time-course estimates,” IEEE Transactions on Biomedical Engineering, vol. 49, no. 12 I, pp. 1534–1546, 2002. View at Publisher · View at Google Scholar
  86. B. Lutkenhoner and R. Grave de Peralta Menendez, “The resolution-field concept,” Electroencephalography and Clinical Neurophysiology, vol. 102, no. 4, pp. 326–334, 1997. View at Publisher · View at Google Scholar
  87. R. Grave de Peralta Menendez, M. M. Murray, C. M. Michel, R. Martuzzi, and S. L. Gonzalez Andino, “Electrical neuroimaging based on biophysical constraints,” NeuroImage, vol. 21, no. 2, pp. 527–539, 2004. View at Publisher · View at Google Scholar
  88. N. K. Logothetis and B. A. Wandell, “Interpreting the BOLD signal,” Annual Review of Physiology, vol. 66, pp. 735–769, 2004. View at Publisher · View at Google Scholar
  89. S. L. Gonzalez Andino, R. Grave de Peralta Menendez, A. Khateb, A. J. Pegna, G. Thut, and T. Landis, “A glimpse into your vision,” Human Brain Mapping, vol. 28, no. 7, pp. 614–624, 2007. View at Publisher · View at Google Scholar
  90. S. L. Gonzalez Andino, C. M. Michel, G. Thut, T. Landis, and R. Grave de Peralta Menendez, “Prediction of response speed by anticipatory high-frequency (gamma band) oscillations in the human brain,” Human Brain Mapping, vol. 24, no. 1, pp. 50–58, 2005. View at Publisher · View at Google Scholar