Independence: a new criterion for the analysis of the electromagnetic fields in the global brain?

The impressive increase in the understanding of some basic processing in the human brain has recently led to the formulation of efficient computational methods, which when applied in the design of better signal processing tools, provides a deeper and clearer view to study the functioning of the human brain. The recently developed independent component analysis (ICA) has been shown to be an efficient tool for artifact identification and extraction from electroencephalographic and magnetoencephalographic recordings. In addition, ICA has been applied to the analysis of brain signals evoked by sensory stimuli. Extensions of the basic ICA methodology have also been employed to reveal otherwise hidden information. This paper reviews our recent results in this field.

[1]  Allan Kardec Barros,et al.  Extraction of event-related signals from multichannel bioelectrical measurements , 2000, IEEE Trans. Biomed. Eng..

[2]  D. Carpenter,et al.  Biological effects of electric and magnetic fields , 1994 .

[3]  Allan Kardec Barros,et al.  Application of ICA in the Separation of Breathing Artifacts in ECG Signal , 1998, ICONIP.

[4]  Erkki Oja,et al.  Image Feature Extraction Using Independent Component Analysis , 1996 .

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

[6]  Aapo Hyvärinen,et al.  Gaussian moments for noisy independent component analysis , 1999, IEEE Signal Processing Letters.

[7]  Te-Won Lee,et al.  Independent Component Analysis , 1998, Springer US.

[8]  J. Le,et al.  Method to reduce blur distortion from EEG's using a realistic head model , 1993, IEEE Transactions on Biomedical Engineering.

[9]  Christian Jutten,et al.  Blind separation of sources, part I: An adaptive algorithm based on neuromimetic architecture , 1991, Signal Process..

[10]  T. E. Katila.  Round table. On the current multipole presentation of the primary current distributions , 1983 .

[11]  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.

[12]  Erkki Oja,et al.  Independent component analysis: algorithms and applications , 2000, Neural Networks.

[13]  M. Girolami,et al.  Advances in Independent Component Analysis , 2000, Perspectives in Neural Computing.

[14]  Kari Torkkola,et al.  Blind Separation For Audio Signals - Are We There Yet? , 1999 .

[15]  David J. Field,et al.  Emergence of simple-cell receptive field properties by learning a sparse code for natural images , 1996, Nature.

[16]  Jean-Franois Cardoso.  High-Order Contrasts for Independent Component Analysis , 1999, Neural Computation.

[17]  T. Sejnowski,et al.  Independent component analysis of fMRI data: Examining the assumptions , 1998, Human brain mapping.

[18]  Pierre Comon,et al.  Independent component analysis, A new concept? , 1994, Signal Process..

[19]  Tzyy-Ping Jung,et al.  Extended ICA Removes Artifacts from Electroencephalographic Recordings , 1997, NIPS.

[20]  R N Vigário,et al.  Extraction of ocular artefacts from EEG using independent component analysis. , 1997, Electroencephalography and clinical neurophysiology.

[21]  V. Jousmäki,et al.  Somatosensory evoked fields to large-area vibrotactile stimuli , 1999, Clinical Neurophysiology.

[22]  B. Josephson.  Possible new effects in superconductive tunnelling , 1962 .

[23]  Jean-Francois Cardoso,et al.  Blind signal separation: statistical principles , 1998, Proc. IEEE.

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

[25]  Shun-ichi Amari,et al.  Adaptive blind signal processing-neural network approaches , 1998, Proc. IEEE.

[26]  Ali Mansour,et al.  Adaptive blind elimination of artifacts in ECG signals , 1998 .

[27]  Erkki Oja,et al.  Independent Component Analysis for Identification of Artifacts in Magnetoencephalographic Recordings , 1997, NIPS.

[28]  Joachim Mocks,et al.  Correcting ocular artifacts in the EEG: A comparison of several methods , 1989 .

[29]  Erkki Oja,et al.  Independent component approach to the analysis of EEG and MEG recordings , 2000, IEEE Transactions on Biomedical Engineering.

[30]  R. Quiroga,et al.  Stationarity of the EEG series , 1995 .

[31]  R. Leahy,et al.  Multiple Dipole Modeling and Localization from , 1992 .

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

[33]  Erkki Oja,et al.  A class of neural networks for independent component analysis , 1997, IEEE Trans. Neural Networks.

[34]  Kevin H. Knuth,et al.  Bayesian source separation and localization , 1998, Optics & Photonics.

[35]  Andreas Ziehe,et al.  TDSEP — an efficient algorithm for blind separation using time structure , 1998 .

[36]  A. Walker.  Electroencephalography, Basic Principles, Clinical Applications and Related Fields , 1982 .

[37]  M. Scherg,et al.  Two bilateral sources of the late AEP as identified by a spatio-temporal dipole model. , 1985, Electroencephalography and clinical neurophysiology.

[38]  R. Ilmoniemi,et al.  Magnetoencephalography-theory, instrumentation, and applications to noninvasive studies of the working human brain , 1993 .

[39]  P Berg,et al.  A multiple source approach to the correction of eye artifacts. , 1994, Electroencephalography and clinical neurophysiology.

[40]  Erkki Oja,et al.  Independent Component Analysis in Wave Decomposition of Auditory Evoked Fields , 1998 .

[41]  D. Chakrabarti,et al.  A fast fixed - point algorithm for independent component analysis , 1997 .