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

Gerd Wübbeler; Andreas Ziehe; Bruno Marcel Mackert; Klaus Robert Müller; Lutz Trahms; Gabriel Curio

doi:10.1109/10.841331

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

Gerd Wübbeler, Andreas Ziehe, Bruno Marcel Mackert, Klaus Robert Müller, Lutz Trahms, Gabriel Curio

Research output: Contribution to journal › Review article › peer-review

50 Citations (Scopus)

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	https://doi.org/10.1109/10.841331
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

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10.1109/10.841331

Cite this

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title = "Independent component analysis of noninvasively recorded cortical magnetic DC-fields in humans",

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.",

keywords = "Biomagnetism, Biomedical data processing, Blind source separation, Independent component analysis, Magnetoencephalography (MEG), dc- recordings",

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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. W{\"u}bbeler, 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.",

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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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JF - IEEE Transactions on Biomedical Engineering

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

Abstract

Bibliographical note

Keywords

ASJC Scopus subject areas

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