An ensemble de-noising method for spatio-temporal EEG and MEG data

Stephan Weiss, Richard Leahy, John Mosher, Robert Stewart

Research output: Contribution to journalArticle

Abstract

EEG/MEG are important tools for non-invasive medical diagnosis and basic studies of the brain and its functioning, but often applications are limited due to a very low SNR in the data. Here, we present a discrete wavelet transform (DWT) based de-noising method for spatio-temporal EEG/MEG measurements collected by a sensor array. A robust threshold selection can be achieved by incorporating spatial information and pre-stimulus data to estimate signal and noise energies. Further improvement can be gained by applying a translation-invariant approach to the derived de-noising scheme. In simulations, the performance of the proposed method is evaluated in comparison to standard de-noising and low-rank approximation, which o ers some complementarity to our approach.
LanguageEnglish
Pages142-153
Number of pages12
JournalEURASIP Journal on Advances in Signal Processing
Volume4
Issue number4
Publication statusPublished - 1997

Fingerprint

Electroencephalography
Discrete wavelet transforms
Sensor arrays
Brain

Keywords

  • signal processing
  • de-noising
  • spatio-temporal EEG

Cite this

@article{3faf95f43e454b2794587897dd4b40af,
title = "An ensemble de-noising method for spatio-temporal EEG and MEG data",
abstract = "EEG/MEG are important tools for non-invasive medical diagnosis and basic studies of the brain and its functioning, but often applications are limited due to a very low SNR in the data. Here, we present a discrete wavelet transform (DWT) based de-noising method for spatio-temporal EEG/MEG measurements collected by a sensor array. A robust threshold selection can be achieved by incorporating spatial information and pre-stimulus data to estimate signal and noise energies. Further improvement can be gained by applying a translation-invariant approach to the derived de-noising scheme. In simulations, the performance of the proposed method is evaluated in comparison to standard de-noising and low-rank approximation, which o ers some complementarity to our approach.",
keywords = "signal processing , de-noising , spatio-temporal EEG",
author = "Stephan Weiss and Richard Leahy and John Mosher and Robert Stewart",
year = "1997",
language = "English",
volume = "4",
pages = "142--153",
journal = "EURASIP Journal on Advances in Signal Processing",
issn = "1110-8657",
number = "4",

}

An ensemble de-noising method for spatio-temporal EEG and MEG data. / Weiss, Stephan; Leahy, Richard ; Mosher, John; Stewart, Robert.

In: EURASIP Journal on Advances in Signal Processing, Vol. 4, No. 4, 1997, p. 142-153.

Research output: Contribution to journalArticle

TY - JOUR

T1 - An ensemble de-noising method for spatio-temporal EEG and MEG data

AU - Weiss, Stephan

AU - Leahy, Richard

AU - Mosher, John

AU - Stewart, Robert

PY - 1997

Y1 - 1997

N2 - EEG/MEG are important tools for non-invasive medical diagnosis and basic studies of the brain and its functioning, but often applications are limited due to a very low SNR in the data. Here, we present a discrete wavelet transform (DWT) based de-noising method for spatio-temporal EEG/MEG measurements collected by a sensor array. A robust threshold selection can be achieved by incorporating spatial information and pre-stimulus data to estimate signal and noise energies. Further improvement can be gained by applying a translation-invariant approach to the derived de-noising scheme. In simulations, the performance of the proposed method is evaluated in comparison to standard de-noising and low-rank approximation, which o ers some complementarity to our approach.

AB - EEG/MEG are important tools for non-invasive medical diagnosis and basic studies of the brain and its functioning, but often applications are limited due to a very low SNR in the data. Here, we present a discrete wavelet transform (DWT) based de-noising method for spatio-temporal EEG/MEG measurements collected by a sensor array. A robust threshold selection can be achieved by incorporating spatial information and pre-stimulus data to estimate signal and noise energies. Further improvement can be gained by applying a translation-invariant approach to the derived de-noising scheme. In simulations, the performance of the proposed method is evaluated in comparison to standard de-noising and low-rank approximation, which o ers some complementarity to our approach.

KW - signal processing

KW - de-noising

KW - spatio-temporal EEG

M3 - Article

VL - 4

SP - 142

EP - 153

JO - EURASIP Journal on Advances in Signal Processing

T2 - EURASIP Journal on Advances in Signal Processing

JF - EURASIP Journal on Advances in Signal Processing

SN - 1110-8657

IS - 4

ER -