A resampling approach to estimate the stability of one-dimensional or multidimensional independent components

Frank Meinecke; Andreas Ziehe; Motoaki Kawanabe; Klaus Robert Müller

doi:10.1109/TBME.2002.805480

A resampling approach to estimate the stability of one-dimensional or multidimensional independent components

Frank Meinecke
, Andreas Ziehe
, Motoaki Kawanabe
, Klaus Robert Müller^*

^*Corresponding author for this work

Research output: Contribution to journal › Article › peer-review

82 Citations (Scopus)

Abstract

When applying unsupervised learning techniques in biomedical data analysis, a key question is whether the estimated parameters of the studied system are reliable. In other words, can we assess the quality of the result produced by our learning technique? We propose resampling methods to tackle this question and illustrate their usefulness for blind-source separation (BSS). We demonstrate that our proposed reliability estimation can be used to discover stable one-dimensional or multidimensional independent components, to choose the appropriate BSS-model, to enhance significantly the separation performance, and, most importantly, to flag components that carry physical meaning. Application to different biomedical testbed data sets (magnetoencephalography (MEG)/electrocardiography (ECG)-recordings) underline the usefulness of our approach.

Original language	English
Pages (from-to)	1514-1525
Number of pages	12
Journal	IEEE Transactions on Biomedical Engineering
Volume	49
Issue number	12 I
DOIs	https://doi.org/10.1109/TBME.2002.805480
Publication status	Published - 2002 Dec 1
Externally published	Yes

Bibliographical note

Funding Information:
Manuscript received December 20, 2001; revised June 3, 2002. The work of K.-R. Müller and A. Ziehe was supported in part by the EU Project (IST-1999-14190—BLISS). The studies were supported by the Bundesmin-isterium für Bildung und Forschung (BMBF) under Grant FKZ 01IBB02A and Grant 01IBB02B. This work is an extension of previous conference publications. Asterisk indicates corresponding author.

Keywords

Blind-source separation
Bootstrap
Electrocardiography (ECG)
Independent component analysis
Magnetoencephalography (MEG)
Multidimensional independent component analysis (ICA)
Reliability
Resampling
Stability
Unsupervised learning

ASJC Scopus subject areas

Biomedical Engineering

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10.1109/TBME.2002.805480

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title = "A resampling approach to estimate the stability of one-dimensional or multidimensional independent components",

abstract = "When applying unsupervised learning techniques in biomedical data analysis, a key question is whether the estimated parameters of the studied system are reliable. In other words, can we assess the quality of the result produced by our learning technique? We propose resampling methods to tackle this question and illustrate their usefulness for blind-source separation (BSS). We demonstrate that our proposed reliability estimation can be used to discover stable one-dimensional or multidimensional independent components, to choose the appropriate BSS-model, to enhance significantly the separation performance, and, most importantly, to flag components that carry physical meaning. Application to different biomedical testbed data sets (magnetoencephalography (MEG)/electrocardiography (ECG)-recordings) underline the usefulness of our approach.",

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note = "Funding Information: Manuscript received December 20, 2001; revised June 3, 2002. The work of K.-R. M{\"u}ller and A. Ziehe was supported in part by the EU Project (IST-1999-14190—BLISS). The studies were supported by the Bundesmin-isterium f{\"u}r Bildung und Forschung (BMBF) under Grant FKZ 01IBB02A and Grant 01IBB02B. This work is an extension of previous conference publications. Asterisk indicates corresponding author.",

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AU - Müller, Klaus Robert

N1 - Funding Information: Manuscript received December 20, 2001; revised June 3, 2002. The work of K.-R. Müller and A. Ziehe was supported in part by the EU Project (IST-1999-14190—BLISS). The studies were supported by the Bundesmin-isterium für Bildung und Forschung (BMBF) under Grant FKZ 01IBB02A and Grant 01IBB02B. This work is an extension of previous conference publications. Asterisk indicates corresponding author.

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N2 - When applying unsupervised learning techniques in biomedical data analysis, a key question is whether the estimated parameters of the studied system are reliable. In other words, can we assess the quality of the result produced by our learning technique? We propose resampling methods to tackle this question and illustrate their usefulness for blind-source separation (BSS). We demonstrate that our proposed reliability estimation can be used to discover stable one-dimensional or multidimensional independent components, to choose the appropriate BSS-model, to enhance significantly the separation performance, and, most importantly, to flag components that carry physical meaning. Application to different biomedical testbed data sets (magnetoencephalography (MEG)/electrocardiography (ECG)-recordings) underline the usefulness of our approach.

AB - When applying unsupervised learning techniques in biomedical data analysis, a key question is whether the estimated parameters of the studied system are reliable. In other words, can we assess the quality of the result produced by our learning technique? We propose resampling methods to tackle this question and illustrate their usefulness for blind-source separation (BSS). We demonstrate that our proposed reliability estimation can be used to discover stable one-dimensional or multidimensional independent components, to choose the appropriate BSS-model, to enhance significantly the separation performance, and, most importantly, to flag components that carry physical meaning. Application to different biomedical testbed data sets (magnetoencephalography (MEG)/electrocardiography (ECG)-recordings) underline the usefulness of our approach.

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KW - Bootstrap

KW - Electrocardiography (ECG)

KW - Independent component analysis

KW - Magnetoencephalography (MEG)

KW - Multidimensional independent component analysis (ICA)

KW - Reliability

KW - Resampling

KW - Stability

KW - Unsupervised learning

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M3 - Article

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SN - 0018-9294

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EP - 1525

JO - IEEE Transactions on Biomedical Engineering

JF - IEEE Transactions on Biomedical Engineering

IS - 12 I

ER -

A resampling approach to estimate the stability of one-dimensional or multidimensional independent components

Abstract

Bibliographical note

Keywords

ASJC Scopus subject areas

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