TY - JOUR
T1 - Independent component analysis of noninvasively recorded cortical magnetic DC-fields in humans
AU - Wübbeler, Gerd
AU - Ziehe, Andreas
AU - Mackert, Bruno Marcel
AU - Müller, Klaus Robert
AU - Trahms, Lutz
AU - Curio, Gabriel
N1 - Funding Information:
Manuscript received July 22, 1999; revised January 6, 2000. The work of A. Ziehe was supported in part by the DFG under Contracts JA 379/52 and JA 379/71. The work of G. Wübbeler, B.-M. Mackert, and G. Curio was supported in part by the DFG under Grants Cu 36/1-1 and 1-2. Asterisk indicates corresponding author.
PY - 2000
Y1 - 2000
N2 - 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.
AB - 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.
KW - Biomagnetism
KW - Biomedical data processing
KW - Blind source separation
KW - Independent component analysis
KW - Magnetoencephalography (MEG)
KW - dc- recordings
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U2 - 10.1109/10.841331
DO - 10.1109/10.841331
M3 - Review article
C2 - 10851803
AN - SCOPUS:0034025820
VL - 47
SP - 594
EP - 599
JO - IEEE Transactions on Biomedical Engineering
JF - IEEE Transactions on Biomedical Engineering
SN - 0018-9294
IS - 5
ER -