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Computational Intelligence and Neuroscience
Volume 2010, Article ID 329436, 12 pages
http://dx.doi.org/10.1155/2010/329436
Research Article

Independent Component Analysis for Source Localization of EEG Sleep Spindle Components

1Department of Medical Instrumentation Technology, Technological Educational Institution of Athens, Ag. Spyridonos Street, Egaleo,12210 Athens, Greece
2Sleep Research Unit, Eginition Hospital, Department of Psychiatry, University of Athens, 74 Vas.Sophias Avenue, 11528 Athens, Greece

Received 15 June 2009; Revised 24 November 2009; Accepted 19 January 2010

Academic Editor: Sara Gonzalez Andino

Copyright © 2010 Erricos M. Ventouras 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. L. De Gennaro and M. Ferrara, “Sleep spindles: an overview,” Sleep Medicine Reviews, vol. 7, no. 5, pp. 423–440, 2003. View at Publisher · View at Google Scholar
  2. J. Zeitlhofer, G. Gruber, P. Anderer, S. Asenbaum, P. Schimicek, and B. Saletu, “Topographic distribution of sleep spindles in young healthy subjects,” Journal of Sleep Research, vol. 6, no. 3, pp. 149–155, 1997. View at Google Scholar
  3. H. Tanaka, M. Hayashi, and T. Hori, “Topographical characteristics and principal component structure of the hypnagogic EEG,” Sleep, vol. 20, no. 7, pp. 523–534, 1997. View at Google Scholar
  4. J. S. Zygierewicz, K. J. Blinowska, P. J. Durka, W. Szelenberger, S. Niemcewicz, and W. Androsiuk, “High resolution study of sleep spindles,” Clinical Neurophysiology, vol. 110, no. 12, pp. 2136–2147, 1999. View at Publisher · View at Google Scholar
  5. E. Werth, P. Achermann, D.-J. Dijk, and A. A. Borbely, “Spindle frequency activity in the sleep EEG: individual differences and topographic distribution,” Electroencephalography and Clinical Neurophysiology, vol. 103, no. 5, pp. 535–542, 1997. View at Publisher · View at Google Scholar
  6. A. Hyvärinen, “Survey on independent component analysis,” Neural Computing Surveys, vol. 2, pp. 94–128, 1999. View at Google Scholar
  7. C. J. James and C. W. Hesse, “Independent component analysis for biomedical signals,” Physiological Measurement, vol. 26, no. 1, pp. R15–R39, 2005. View at Publisher · View at Google Scholar
  8. P. Comon, “Independent component analysis, a new concept?” Signal Processing, vol. 36, no. 3, pp. 287–314, 1994. View at Google Scholar
  9. S. Makeig, M. Westerfield, T.-P. Jung et al., “Functionally independent components of the late positive event-related potential during visual spatial attention,” Journal of Neuroscience, vol. 19, no. 7, pp. 2665–2680, 1999. View at Google Scholar
  10. J. Onton, M. Westerfield, J. Townsend, and S. Makeig, “Imaging human EEG dynamics using independent component analysis,” Neuroscience and Biobehavioral Reviews, vol. 30, no. 6, pp. 808–822, 2006. View at Publisher · View at Google Scholar · View at PubMed
  11. B. Jervis, S. Belal, K. Camilleri et al., “The independent components of auditory P300 and CNV evoked potentials derived from single-trial recordings,” Physiological Measurement, vol. 28, no. 8, pp. 745–771, 2007. View at Publisher · View at Google Scholar · View at PubMed
  12. E. M. Ventouras, I. Alevizos, P. Y. Ktonas et al., “Independent components of sleep spindles,” in Proceedings of the 29th Annual International Conference of the IEEE Engineering in Medicine and Biology (EMBC '07), pp. 4002–4005, Lyon, France, August 2007. View at Publisher · View at Google Scholar · View at PubMed
  13. C. M. Michel, M. M. Murray, G. Lantz, S. Gonzalez, L. Spinelli, and R. Grave de Peralta, “EEG source imaging,” Clinical Neurophysiology, vol. 115, no. 10, pp. 2195–2222, 2004. View at Publisher · View at Google Scholar · View at PubMed
  14. R. D. Pascual-Marqui, C. M. Michel, and D. Lehmann, “Low resolution electromagnetic tomography: a new method for localizing electrical activity in the brain,” International Journal of Psychophysiology, vol. 18, no. 1, pp. 49–65, 1994. View at Publisher · View at Google Scholar
  15. R. D. Pascual-Marqui, D. Lehmann, T. Koenig et al., “Low resolution brain electromagnetic tomography (LORETA) functional imaging in acute, neuroleptic-naive, first-episode, productive schizophrenia,” Psychiatry Research: Neuroimaging, vol. 90, no. 3, pp. 169–179, 1999. View at Publisher · View at Google Scholar
  16. J. Talairach and P. Tournoux, Co-Planar Stereotaxic Atlas of the Human Brain, Thieme, New York, NY, USA, 1988.
  17. P. Anderer, G. Klosch, 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
  18. P. J. 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 · View at PubMed
  19. M. Nakamura, S. Uchida, T. Maehara et al., “Sleep spindles in human prefrontal cortex: an electrocorticographic study,” Neuroscience Research, vol. 45, no. 4, pp. 419–427, 2003. View at Publisher · View at Google Scholar
  20. M. Steriade and F. Amzica, “Coalescence of sleep rhythms and their chronology in corticothalamic networks,” Sleep Research Online, vol. 1, no. 1, pp. 1–10, 1998. View at Google Scholar
  21. V. Gumenyuk, T. Roth, J. E. Moran et al., “Cortical locations of maximal spindle activity: magnetoencephalography (MEG) study,” Journal of Sleep Research, vol. 18, no. 2, pp. 245–253, 2009. View at Publisher · View at Google Scholar · View at PubMed
  22. H. Merica and R. D. Fortune, “A neuronal transition probability model for the evolution of power in the sigma and delta frequency bands of sleep EEG,” Physiology and Behavior, vol. 62, no. 3, pp. 585–589, 1997. View at Publisher · View at Google Scholar
  23. V. Knoblauch, W. L. J. Martens, A. Wirz-Justice, and C. Cajochen, “Human sleep spindle characteristics after sleep deprivation,” Clinical Neurophysiology, vol. 114, no. 12, pp. 2258–2267, 2003. View at Publisher · View at Google Scholar
  24. L. De Gennaro, M. Ferrara, F. Vecchio, G. Curcio, and M. Bertini, “An electroencephalographic fingerprint of human sleep,” NeuroImage, vol. 26, no. 1, pp. 114–122, 2005. View at Publisher · View at Google Scholar · View at PubMed
  25. A. Rechtschaffen and A. Kales, Eds., A Manual of Standardized Terminology, Techniques and Scoring System for Sleep Stages of Human Subjects, Public Health Service, U.S. Government Printing Office, Washington, DC, USA, 1968.
  26. A. Hyvärinen, “Fast and robust fixed-point algorithms for independent component analysis,” IEEE Transactions on Neural Networks, vol. 10, no. 3, pp. 626–634, 1999. View at Publisher · View at Google Scholar · View at PubMed
  27. E. M. Ventouras, P. Y. Ktonas, H. Tsekou, T. Paparrigopoulos, I. Kalatzis, and C. R. Soldatos, “Slow and fast EEG sleep spindle component extraction using Independent Component Analysis,” in Proceedings of the 8th IEEE International Conference on BioInformatics and BioEngineering (BIBE '08), Athens, Greece, October 2008. View at Publisher · View at Google Scholar
  28. A. J. Bell and T. J. Sejnowski, “An information-maximization approach to blind separation and blind deconvolution,” Neural computation, vol. 7, no. 6, pp. 1129–1159, 1995. View at Google Scholar
  29. J. F. Cardoso and A. Souloumiac, “Blind beamforming for non-Gaussian signals,” IEE Proceedings F, vol. 140, no. 6, pp. 362–370, 1993. View at Google Scholar
  30. A. Delorme and S. Makeig, “EEGLAB: an open source toolbox for analysis of single-trial EEG dynamics including independent component analysis,” Journal of Neuroscience Methods, vol. 134, no. 1, pp. 9–21, 2004. View at Publisher · View at Google Scholar · View at PubMed
  31. A. Hyvärinen and E. Oja, “Independent component analysis: algorithms and applications,” Neural Networks, vol. 13, no. 4-5, pp. 411–430, 2000. View at Publisher · View at Google Scholar
  32. R. Vigario, J. Sarela, and E. Oja, “Independent component analysis in wave decomposition of auditory evoked fields,” in Proceedings of the International Conference on Artificial Neural Networks (ICANN '98), pp. 287–292, Skovde, Sweden, 1998.
  33. I. Manshanden, J. C. de Munck, N. R. Simon, and F. H. Lopes da Silva, “Source localization of MEG sleep spindles and the relation to sources of alpha band rhythms,” Clinical Neurophysiology, vol. 113, no. 12, pp. 1937–1947, 2002. View at Publisher · View at Google Scholar
  34. R. Ferri, O. Bruni, S. Miano, and M. G. Terzano, “Topographic mapping of the spectral components of the cyclic alternating pattern (CAP),” Sleep Medicine, vol. 6, no. 1, pp. 29–36, 2005. View at Publisher · View at Google Scholar · View at PubMed
  35. M. Toth, B. Faludi, J. Wackermann, J. Czopf, and I. Kondakor, “Characteristic changes in brain electrical activity due to chronic hypoxia in patients with obstructive sleep apnea syndrome (OSAS): a combined EEG study using LORETA and omega complexity,” Brain Topography, vol. 22, no. 3, pp. 185–190, 2009. View at Publisher · View at Google Scholar · View at PubMed
  36. M. Saletu, P. Anderer, G. M. Saletu-Zyhlarz, M. Mandl, B. Saletu, and J. Zeitlhofer, “Modafinil improves information processing speed and increases energetic resources for orientation of attention in narcoleptics: double-blind, placebo-controlled ERP studies with low-resolution brain electromagnetic tomography (LORETA),” Sleep Medicine, vol. 10, no. 8, pp. 850–858, 2009. View at Publisher · View at Google Scholar · View at PubMed
  37. A. Sinai and H. Pratt, “High-resolution time course of hemispheric dominance revealed by low-resolution electromagnetic tomography,” Clinical Neurophysiology, vol. 114, no. 7, pp. 1181–1188, 2003. View at Publisher · View at Google Scholar
  38. M. J. Herrmann, J. Rommler, A.-C. Ehlis, A. Heidrich, and A. J. Fallgatter, “Source localization (LORETA) of the error-related-negativity (ERN/Ne) and positivity (Pe),” Cognitive Brain Research, vol. 20, no. 2, pp. 294–299, 2004. View at Publisher · View at Google Scholar · View at PubMed
  39. 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 · View at PubMed
  40. C. Phillips, J. Mattout, M. D. Rugg, P. Maquet, and K. J. Friston, “An empirical Bayesian solution to the source reconstruction problem in EEG,” NeuroImage, vol. 24, no. 4, pp. 997–1011, 2005. View at Publisher · View at Google Scholar · View at PubMed