Enhancing the signal-to-noise ratio of ICA-based extracted ERPs

Steven Lemm; Gabriel Curio; Yevhen Hlushchuk; Klaus Robert Müller

doi:10.1109/TBME.2006.870258

Enhancing the signal-to-noise ratio of ICA-based extracted ERPs

Steven Lemm^*
, Gabriel Curio
, Yevhen Hlushchuk
, Klaus Robert Müller

^*Corresponding author for this work

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

75 Citations (Scopus)

Abstract

When decomposing single trial electroencephalography it is a challenge to incorporate prior physiological knowledge. Here, we develop a method that uses prior information about the phase-locking property of event-related potentials in a regularization framework to bias a blind source separation algorithm toward an improved separation of single-trial phase-locked responses in terms of an increased signal-to-noise ratio. In particular, we suggest a transformation of the data, using weighted average of the single trial and trial-averaged response, that redirects the focus of source separation methods onto the subspace of event-related potentials. The practical benefit with respect to an improved separation of such components from ongoing background activity and extraneous noise is first illustrated on artificial data and finally verified in a real-world application of extracting single-trial somatosensory evoked potentials from multichannel EEG-recordings.

Original language	English
Article number	1608509
Pages (from-to)	601-607
Number of pages	7
Journal	IEEE Transactions on Biomedical Engineering
Volume	53
Issue number	4
DOIs	https://doi.org/10.1109/TBME.2006.870258
Publication status	Published - 2006 Apr
Externally published	Yes

Bibliographical note

Funding Information:
Manuscript received August 11, 2004; revised July 4, 2005. This work was supported by in part by the DFG SFB 618/B4 and by the Academy of Finland, The Ministry of Education Finland via the Finnish Graduate School of Neuroscience. Asterisk indicates corresponding author. *S. Lemm is with the Department of Intelligent Data Analysis, FIRST Fraun-hofer Institute, 12489 Berlin, Germany and also with the Neurophysics Group, Department of Neurology, Campus Benjamin Franklin, Charité University of Medicine, 12200 Berlin, Germany (e-mail: [email protected]).

Keywords

Bioelectrical potentials
Electroencephalogram (EEG)
Independent component analysis (ICA)
Signal-to-noise ratio

ASJC Scopus subject areas

Biomedical Engineering

Access to Document

10.1109/TBME.2006.870258

Cite this

@article{b218eaebe5fb413c9e5a3d6ad8aacb30,

title = "Enhancing the signal-to-noise ratio of ICA-based extracted ERPs",

abstract = "When decomposing single trial electroencephalography it is a challenge to incorporate prior physiological knowledge. Here, we develop a method that uses prior information about the phase-locking property of event-related potentials in a regularization framework to bias a blind source separation algorithm toward an improved separation of single-trial phase-locked responses in terms of an increased signal-to-noise ratio. In particular, we suggest a transformation of the data, using weighted average of the single trial and trial-averaged response, that redirects the focus of source separation methods onto the subspace of event-related potentials. The practical benefit with respect to an improved separation of such components from ongoing background activity and extraneous noise is first illustrated on artificial data and finally verified in a real-world application of extracting single-trial somatosensory evoked potentials from multichannel EEG-recordings.",

keywords = "Bioelectrical potentials, Electroencephalogram (EEG), Independent component analysis (ICA), Signal-to-noise ratio",

author = "Steven Lemm and Gabriel Curio and Yevhen Hlushchuk and M{\"u}ller, \{Klaus Robert\}",

note = "Funding Information: Manuscript received August 11, 2004; revised July 4, 2005. This work was supported by in part by the DFG SFB 618/B4 and by the Academy of Finland, The Ministry of Education Finland via the Finnish Graduate School of Neuroscience. Asterisk indicates corresponding author. *S. Lemm is with the Department of Intelligent Data Analysis, FIRST Fraun-hofer Institute, 12489 Berlin, Germany and also with the Neurophysics Group, Department of Neurology, Campus Benjamin Franklin, Charit{\'e} University of Medicine, 12200 Berlin, Germany (e-mail: [email protected]).",

year = "2006",

month = apr,

doi = "10.1109/TBME.2006.870258",

language = "English",

volume = "53",

pages = "601--607",

journal = "IEEE Transactions on Biomedical Engineering",

issn = "0018-9294",

publisher = "IEEE Computer Society",

number = "4",

}

TY - JOUR

T1 - Enhancing the signal-to-noise ratio of ICA-based extracted ERPs

AU - Lemm, Steven

AU - Curio, Gabriel

AU - Hlushchuk, Yevhen

AU - Müller, Klaus Robert

N1 - Funding Information: Manuscript received August 11, 2004; revised July 4, 2005. This work was supported by in part by the DFG SFB 618/B4 and by the Academy of Finland, The Ministry of Education Finland via the Finnish Graduate School of Neuroscience. Asterisk indicates corresponding author. *S. Lemm is with the Department of Intelligent Data Analysis, FIRST Fraun-hofer Institute, 12489 Berlin, Germany and also with the Neurophysics Group, Department of Neurology, Campus Benjamin Franklin, Charité University of Medicine, 12200 Berlin, Germany (e-mail: [email protected]).

PY - 2006/4

Y1 - 2006/4

N2 - When decomposing single trial electroencephalography it is a challenge to incorporate prior physiological knowledge. Here, we develop a method that uses prior information about the phase-locking property of event-related potentials in a regularization framework to bias a blind source separation algorithm toward an improved separation of single-trial phase-locked responses in terms of an increased signal-to-noise ratio. In particular, we suggest a transformation of the data, using weighted average of the single trial and trial-averaged response, that redirects the focus of source separation methods onto the subspace of event-related potentials. The practical benefit with respect to an improved separation of such components from ongoing background activity and extraneous noise is first illustrated on artificial data and finally verified in a real-world application of extracting single-trial somatosensory evoked potentials from multichannel EEG-recordings.

AB - When decomposing single trial electroencephalography it is a challenge to incorporate prior physiological knowledge. Here, we develop a method that uses prior information about the phase-locking property of event-related potentials in a regularization framework to bias a blind source separation algorithm toward an improved separation of single-trial phase-locked responses in terms of an increased signal-to-noise ratio. In particular, we suggest a transformation of the data, using weighted average of the single trial and trial-averaged response, that redirects the focus of source separation methods onto the subspace of event-related potentials. The practical benefit with respect to an improved separation of such components from ongoing background activity and extraneous noise is first illustrated on artificial data and finally verified in a real-world application of extracting single-trial somatosensory evoked potentials from multichannel EEG-recordings.

KW - Bioelectrical potentials

KW - Electroencephalogram (EEG)

KW - Independent component analysis (ICA)

KW - Signal-to-noise ratio

UR - https://www.scopus.com/pages/publications/33645281969

U2 - 10.1109/TBME.2006.870258

DO - 10.1109/TBME.2006.870258

M3 - Article

C2 - 16602566

AN - SCOPUS:33645281969

SN - 0018-9294

VL - 53

SP - 601

EP - 607

JO - IEEE Transactions on Biomedical Engineering

JF - IEEE Transactions on Biomedical Engineering

IS - 4

M1 - 1608509

ER -

Enhancing the signal-to-noise ratio of ICA-based extracted ERPs

Abstract

Bibliographical note

Keywords

ASJC Scopus subject areas

Access to Document

Other files and links

Fingerprint

Cite this