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 |
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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 |
Bibliographical note
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.
Keywords
- Biomagnetism
- Biomedical data processing
- Blind source separation
- Independent component analysis
- Magnetoencephalography (MEG)
- dc- recordings
ASJC Scopus subject areas
- Biomedical Engineering