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...

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Bibliographic Details
Published in:IEEE transactions on biomedical engineering 2000-05, Vol.47 (5), p.594-599
Main Authors: Wubbeler, G., Ziehe, A., Mackert, B.-M., Muller, K.-R., Trahms, L., Curio, C.
Format: Article
Language:English
Subjects:
Acoustic Stimulation
Algorithms
Auditory Cortex - physiology
Bioelectric potentials
Biological and medical sciences
Blind source separation
Data analysis
Data mining
Evoked Potentials, Auditory - physiology
Feature extraction
Humans
Independent component analysis
Investigative techniques, diagnostic techniques (general aspects)
Magnetic analysis
Magnetic recording
Magnetic separation
Magnetoencephalography
Medical sciences
Medical services
Nervous system
Neurophysiology
Pathology. Cytology. Biochemistry. Spectrometry. Miscellaneous investigative techniques
Signal Processing, Computer-Assisted
Source separation
Statistical methods
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Summary: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.
ISSN:0018-9294
1558-2531
DOI:10.1109/10.841331