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

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.

[1]  K Richard Ridderinkhof,et al.  ERP components associated with successful and unsuccessful stopping in a stop-signal task. , 2004, Psychophysiology.

[2]  Tyrone D. Cannon,et al.  P300 subcomponent abnormalities in schizophrenia: III. Deficits in unaffected siblings of schizophrenic probands , 2000, Biological Psychiatry.

[3]  D. Friedman,et al.  The novelty P3: an event-related brain potential (ERP) sign of the brain's evaluation of novelty , 2001, Neuroscience & Biobehavioral Reviews.

[4]  Andrzej Cichocki,et al.  Robust learning algorithm for blind separation of signals , 1994 .

[5]  Terrence J. Sejnowski,et al.  An Information-Maximization Approach to Blind Separation and Blind Deconvolution , 1995, Neural Computation.

[6]  J.C. Mosher,et al.  Multiple dipole modeling and localization from spatio-temporal MEG data , 1992, IEEE Transactions on Biomedical Engineering.

[7]  R.M. Leahy,et al.  EEG and MEG source localization using recursively applied (RAP) MUSIC , 1996, Conference Record of The Thirtieth Asilomar Conference on Signals, Systems and Computers.

[8]  J. Polich.  Clinical application of the P300 event-related brain potential. , 2004, Physical medicine and rehabilitation clinics of North America.

[9]  S Makeig,et al.  Blind separation of auditory event-related brain responses into independent components. , 1997, Proceedings of the National Academy of Sciences of the United States of America.

[10]  J. Polich,et al.  P3a and P3b from typical auditory and visual stimuli , 1999, Clinical Neurophysiology.

[11]  J. Daléry,et al.  Alteration of event related potentials in siblings discordant for schizophrenia , 2000, Schizophrenia Research.

[12]  B. Turetsky,et al.  P300 subcomponent abnormalities in schizophrenia: I. Physiological evidence for gender and subtype specific differences in regional pathology , 1998, Biological Psychiatry.

[13]  Benoît Champagne,et al.  Statistical maps for EEG dipolar source localization , 2005, IEEE Transactions on Biomedical Engineering.

[14]  Tzyy-Ping Jung,et al.  Independent Component Analysis of Electroencephalographic Data , 1995, NIPS.

[15]  E. Donchin,et al.  Localization of the event-related potential novelty response as defined by principal components analysis. , 2003, Brain research. Cognitive brain research.

[16]  Carlos H. Muravchik,et al.  A meshless method for solving the EEG forward problem , 2005, IEEE Transactions on Biomedical Engineering.

[17]  J. Sarvas.  Basic mathematical and electromagnetic concepts of the biomagnetic inverse problem. , 1987, Physics in medicine and biology.

[18]  Andrzej Cichocki,et al.  Adaptive Blind Signal and Image Processing - Learning Algorithms and Applications , 2002 .

[19]  J. Polich,et al.  Meta-analysis of P300 and schizophrenia: patients, paradigms, and practical implications. , 2003, Psychophysiology.

[20]  Ulrich Hegerl,et al.  Neurochemical Substrates and Neuroanatomical Generators of the Event-Related P300 , 1999, Neuropsychobiology.

[21]  R. Leahy,et al.  EEG and MEG: forward solutions for inverse methods , 1999, IEEE Transactions on Biomedical Engineering.

[22]  I. D. Coope,et al.  Reliable computation of the points of intersection of $n$ spheres in $R^n$ , 2000 .

[23]  Wei Lu,et al.  Approach and applications of constrained ICA , 2005, IEEE Transactions on Neural Networks.