Abstract
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.
| Original language | English |
|---|---|
| Pages (from-to) | 594-599 |
| Number of pages | 6 |
| Journal | IEEE Transactions on Biomedical Engineering |
| Volume | 47 |
| Issue number | 5 |
| DOIs | |
| Publication status | Published - 2000 Jun 6 |
| Externally published | Yes |
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Keywords
- Biomagnetism
- Biomedical data processing
- Blind source separation
- dc- recordings
- Independent component analysis
- Magnetoencephalography (MEG)
ASJC Scopus subject areas
- Biomedical Engineering
Cite this
Independent component analysis of noninvasively recorded cortical magnetic DC-fields in humans. / Wübbeler, Gerd; Ziehe, Andreas; Mackert, Bruno Marcel; Muller, Klaus; Trahms, Lutz; Curio, Gabriel.
In: IEEE Transactions on Biomedical Engineering, Vol. 47, No. 5, 06.06.2000, p. 594-599.Research output: Contribution to journal › Article
}
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 - Muller, Klaus
AU - Trahms, Lutz
AU - Curio, Gabriel
PY - 2000/6/6
Y1 - 2000/6/6
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 - dc- recordings
KW - Independent component analysis
KW - Magnetoencephalography (MEG)
UR - http://www.scopus.com/inward/record.url?scp=0034025820&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=0034025820&partnerID=8YFLogxK
U2 - 10.1109/10.841331
DO - 10.1109/10.841331
M3 - Article
VL - 47
SP - 594
EP - 599
JO - IEEE Transactions on Biomedical Engineering
JF - IEEE Transactions on Biomedical Engineering
SN - 0018-9294
IS - 5
ER -