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  • Research Article
  • Open Access

Separation and Localisation of P300 Sources and Their Subcomponents Using Constrained Blind Source Separation

EURASIP Journal on Advances in Signal Processing20062007:082912

  • Received: 1 October 2005
  • Accepted: 11 June 2006
  • Published:


Separation and localisation of P300 sources and their constituent subcomponents for both visual and audio stimulations is investigated in this paper. An effective constrained blind source separation (CBSS) algorithm is developed for this purpose. The algorithm is an extension of the Infomax BSS system for which a measure of distance between a carefully measured P300 and the estimated sources is used as a constraint. During separation, the proposed CBSS method attempts to extract the corresponding P300 signals. The locations of the corresponding sources are then estimated with some indeterminancy in the results. It can be seen that the locations of the sources change for a schizophrenic patient. The experimental results verify the statistical significance of the method and its potential application in the diagnosis and monitoring of schizophrenia.


  • Schizophrenia
  • Information Technology
  • Potential Application
  • Quantum Information
  • Schizophrenic Patient

Authors’ Affiliations

The Centre of Digital Signal Processing, School of Engineering, Cardiff University, Queen's Buildings, P.O. Box 925, Newport Road, Wales, Cardiff, CF24 3AA, UK
The Brain Image Analysis Unit, Institute of Psychiatry, King's College Hospital, London, SE5 8AF, UK


  1. Dien J, Spencer K, Donchin E: Localization of the event-related potential novelty response as defined by principal components analysis. Cognitive Brain Research 2003,17(3):637–650. 10.1016/S0926-6410(03)00188-5View ArticleGoogle Scholar
  2. Frodl-Bauch T, Bottlender R, Hegerl U: Neurochemical substrates and neuroanatomical generators of the event-related P300. Neuropsychobiology 1999,40(2):86–94. 10.1159/000026603View ArticleGoogle Scholar
  3. Kok A, Ramautar J, De Ruiter M, Band G, Ridderinkhof K: ERP components associated with successful and unsuccessful stopping in a stop-signal task. Psychophysiology 2004,41(1):9–20. 10.1046/j.1469-8986.2003.00127.xView ArticleGoogle Scholar
  4. Polich J: Clinical application of the P300 event-related brain potential. Physical Medicine and Rehabilitation Clinics of North America 2004,15(1):133–161. 10.1016/S1047-9651(03)00109-8View ArticleGoogle Scholar
  5. Friedman D, Cycowicz Y, Gaeta H: The novelty P3: an event-related brain potential (ERP) sign of the brain's evaluation of novelty. Neuroscience & Biobehavioral Reviews 2001,25(4):355–373. 10.1016/S0149-7634(01)00019-7View ArticleGoogle Scholar
  6. Comerchero M, Polich J: P3a and P3b from typical auditory and visual stimuli. Clinical Neurophysiology 1999,110(1):24–30. 10.1016/S0168-5597(98)00033-1View ArticleGoogle Scholar
  7. Niedermeyer E, da Silva FL: Electroencephalography, Basic Problems, Clinical Applications, and Related Fields. 4th edition. Lippincott Williams & Wilkins, Philadelphia, Pa, USA; 1999.Google Scholar
  8. Turetsky B, Colbath E, Gur R: P300 subcomponent abnormalities in schizophrenia: I. Physiological evidence for gender and subtype specific differences in regional pathology. Biological Psychiatry 1998,43(2):84–96. 10.1016/S0006-3223(97)00258-8View ArticleGoogle Scholar
  9. Turetsky B, Cannon T, Gur R: P300 subcomponent abnormalities in schizophrenia: III. Deficits in unaffected siblings of schizophrenic probands. Biological Psychiatry 2000,47(5):380–390. 10.1016/S0006-3223(99)00290-5View ArticleGoogle Scholar
  10. Jeon Y-W, Polich J: Meta-analysis of P300 and schizophrenia: patients, paradigms, and practical implications. Psychophysiology 2003,40(5):684–701. 10.1111/1469-8986.00070View ArticleGoogle Scholar
  11. Cichocki A, Amari S-I: Adaptive Blind Signal and Image Processing: Learning Algorithms and Applications. John Wiley & Sons, New York, NY, USA; 2002.View ArticleGoogle Scholar
  12. Mosher JC, Lewis PC, Leahy RM: Multiple dipole modeling and localization from spatio-temporal MEGdata. IEEE Transactions on Biomedical Engineering 1992,39(6):541–557. 10.1109/10.141192View ArticleGoogle Scholar
  13. Mosher JC, Leahy RM: EEG and MEG source localization using recursively applied (RAP)music. Proceedings of the 13th Asilomar Conference on Signals, Systems and Computers, November 1996, Pacific Grove, Calif, USA 2: 1201–1207.Google Scholar
  14. Makeig S, Bell AJ, Jung T-P, Sejnowski TJ: Independent component analysis of electroencephalographic data. In Advances in Neural Information Processing Systems. Volume 8. MIT Press, Cambridge, Mass, USA; 1996:145–151.Google Scholar
  15. Makeig S, Jung T-P, Bell AJ, Ghahremani D, Sejnowski TJ: Blind separation of auditory event-related brain responses into independent components. Proceedings of the National Academy of Sciences of the United States of America 1997,94(20):10979–10984. 10.1073/pnas.94.20.10979View ArticleGoogle Scholar
  16. Bell AJ, Sejnowski TJ: An information-maximization approach to blind separation and blind deconvolution. Neural Computation 1995,7(6):1129–1159. 10.1162/neco.1995.7.6.1129View ArticleGoogle Scholar
  17. Lu W, Rajapakse JC: Constrained ICA. In Advances in Neural Information Processing Systems. Volume 13. MIT Press, Cambridge, Mass, USA; 2000.Google Scholar
  18. Lu W, Rajapakse JC: Approach and applications of constrained ICA. IEEE Transactions on Neural Networks 2005,16(1):203–212. 10.1109/TNN.2004.836795View ArticleGoogle Scholar
  19. Cichocki A, Unbehauen R, Rummert E: Robust learning algorithm for blind separation of signals. Electronics Letters 1994,30(17):1386–1387. 10.1049/el:19940956View ArticleGoogle Scholar
  20. Cichocki A, Unbehauen R: Neural Networks for Optimisation and Signal Processing. John Wiley & Sons, New York, NY, USA; 1994.MATHGoogle Scholar
  21. Mosher JC, Leahy RM, Lewis PS: EEG and MEG: forward solutions for inverse methods. IEEE Transactions on Biomedical Engineering 1999,46(3):245–259. 10.1109/10.748978View ArticleGoogle Scholar
  22. Von Ellenrieder N, Muravchik CH, Nehorai A: A meshless method for solving the EEG forward problem. IEEE Transactions on Biomedical Engineering 2005,52(2):249–257. 10.1109/TBME.2004.840499View ArticleGoogle Scholar
  23. Bénar C-G, Gunn RN, Grova C, Champagne B, Gotman J: Statistical maps for EEG dipolar source localization. IEEE Transactions on Biomedical Engineering 2005,52(3):401–413. 10.1109/TBME.2004.841263View ArticleGoogle Scholar
  24. Sarvas J: Basic mathematical and electromagnetic concepts of the biomagnetic inverse problem. Physics in Medicine and Biology 1987,32(1):11–22. 10.1088/0031-9155/32/1/004View ArticleGoogle Scholar
  25. Coope ID: Reliable computation of the points of intersection of n spheres in n-space. ANZIAM Journal 2000,42(5):461–477.MathSciNetView ArticleGoogle Scholar
  26. Karoumi B, Laurent A, Rosenfeld F, et al.: Alteration of event related potentials in siblings discordant for schizophrenia. Schizophrenia Research 2000,41(2):325–334. 10.1016/S0920-9964(99)00062-6View ArticleGoogle Scholar
  27. Yordanova J, Kolev V: The relationship between P300 and event-related theta EEG activity. Psycoloquy 1996.,7(25, Memory Brain (7)):Google Scholar


© Loukianos Spyrou et al. 2007

This article is published under license to BioMed Central Ltd. 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.