Independent component analysis of noninvasively recorded cortical magnetic DC-fields in humans

We apply a recently developed multivariate statistical data analysis technique-so called blind source separation (BSS) by independent component analysis-to process magnetoencephalogram recordings of near-DC fields. The extraction of near-DC fields from MEG recordings has great relevance for medical applications since slowly varying DC-phenomena have been found, e.g., in cerebral anoxia and spreading depression in animals. Comparing several BSS approaches, it turns out that an algorithm based on temporal decorrelation successfully extracted a DC-component which was induced in the auditory cortex by presentation of music. The task is challenging because of the limited amount of available data and the corruption by outliers, which makes it an interesting real-world testbed for studying the robustness of ICA methods.

[1]  Eric Moulines,et al.  A blind source separation technique using second-order statistics , 1997, IEEE Trans. Signal Process..

[2]  B. Ross,et al.  The Auditory Evoked “Off” Response: Sources and Comparison with the"On" and the “Sustained” Responses , 1996, Ear and hearing.

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

[4]  Andrzej Cichocki,et al.  A New Learning Algorithm for Blind Signal Separation , 1995, NIPS.

[5]  Schuster,et al.  Separation of a mixture of independent signals using time delayed correlations. , 1994, Physical review letters.

[6]  Jean-François Cardoso,et al.  Equivariant adaptive source separation , 1996, IEEE Trans. Signal Process..

[7]  Bernhard Schölkopf,et al.  Nonlinear Component Analysis as a Kernel Eigenvalue Problem , 1998, Neural Computation.

[8]  N Tepley,et al.  Magnetoencephalography of Focal Cerebral Ischemia in Rats , 1992, Stroke.

[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]  Aapo Hyvärinen,et al.  Independent component analysis in the presence of Gaussian noise by maximizing joint likelihood , 1998, Neurocomputing.

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

[12]  D. Warner,et al.  Cortical negative DC deflections following middle cerebral artery occlusion and KCl-induced spreading depression: effect on blood flow, tissue oxygenation, and electroencephalogram. , 1994 .

[13]  J. Cardoso,et al.  Blind beamforming for non-gaussian signals , 1993 .

[14]  A. J. Bell,et al.  Blind Separation of Event-Related Brain Responses into Independent Components , 1996 .

[15]  Andreas Ziehe,et al.  Artifact Reduction in Magnetoneurography Based on Time-Delayed Second Order Correlations , 1998 .

[16]  Richard Nuccitelli,et al.  A computerized 2-dimensional vibrating probe for mapping extracellular current patterns , 1992, Journal of Neuroscience Methods.

[17]  D. Drung The PTB 83-SQUID system for biomagnetic applications in a clinic , 1995, IEEE Transactions on Applied Superconductivity.

[18]  Lang Tong,et al.  Indeterminacy and identifiability of blind identification , 1991 .

[19]  K D Wolff,et al.  Non-invasive neuromagnetic monitoring of nerve and muscle injury currents. , 1993, Electroencephalography and clinical neurophysiology.

[20]  P. Philips,et al.  JADETD : COMBINING HIGHER-ORDER STATISTICS AND TEMPORALINFORMATION FOR BLIND SOURCE SEPARATION ( WITH NOISE ) , 1999 .

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

[22]  N. Tepley,et al.  Magnetic fields associated with spreading depression in anaesthetised rabbits , 1991, Brain Research.

[23]  Peter K. Stys,et al.  Compound action potential of nerve recorded by suction electrode: a theoretical and experimental analysis , 1991, Brain Research.

[24]  G. Wübbeler,et al.  SQUID measurements of human nerve and muscle near-DC injury-currents using a mechanical modulation o , 1999 .

[25]  T. Picton,et al.  Human auditory sustained potentials. I. The nature of the response. , 1978, Electroencephalography and clinical neurophysiology.

[26]  Gabriel Curio,et al.  Magnetometry of injury currents from human nerve and muscle specimens using Superconducting Quantum Interferences Devices , 1999, Neuroscience Letters.

[27]  Aapo Hyvärinen,et al.  Fast and robust fixed-point algorithms for independent component analysis , 1999, IEEE Trans. Neural Networks.

[28]  Gabriel Curio,et al.  Non-invasive long-term recordings of cortical ‘direct current’ (DC–) activity in humans using magnetoencephalography , 1999, Neuroscience Letters.

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

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

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