An ensemble de-noising method for spatio-temporal EEG / MEG data
An ensemble de-noising method for spatio-temporal EEG / MEG data
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 offers some complementarity to our approach.
142-153
Weiss, S.
a1716781-351d-41d2-8d67-3e3d34f16476
Leahy, R.M.
7ef4a44c-bdd9-4b10-a668-ffdc407cfd22
Mosher, J.C.
5275c3b0-717e-4ecf-a71a-083dfeb07aa3
Stewart, R.W.
c819965d-70b4-4689-8206-e6e444dcbf8e
1997
Weiss, S.
a1716781-351d-41d2-8d67-3e3d34f16476
Leahy, R.M.
7ef4a44c-bdd9-4b10-a668-ffdc407cfd22
Mosher, J.C.
5275c3b0-717e-4ecf-a71a-083dfeb07aa3
Stewart, R.W.
c819965d-70b4-4689-8206-e6e444dcbf8e
Weiss, S., Leahy, R.M., Mosher, J.C. and Stewart, R.W.
(1997)
An ensemble de-noising method for spatio-temporal EEG / MEG data.
EURASIP Journal on Applied Signal Processing, 4 (3), .
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 offers some complementarity to our approach.
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weiss98d.ps
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Published date: 1997
Organisations:
Electronics & Computer Science
Identifiers
Local EPrints ID: 251912
URI: http://eprints.soton.ac.uk/id/eprint/251912
ISSN: 0941-0635
PURE UUID: 1aeae4eb-ece3-42be-9d35-d08b57104453
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Date deposited: 11 Dec 2003
Last modified: 14 Mar 2024 05:14
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Contributors
Author:
S. Weiss
Author:
R.M. Leahy
Author:
J.C. Mosher
Author:
R.W. Stewart
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