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AnEnsemble De-Noising Method for Spatio-Temporal EEG / MEG Data

Record type: Article

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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Citation

Weiss, S, Leahy, R M, Mosher, J C and Stewart, R W (1997) AnEnsemble De-Noising Method for Spatio-Temporal EEG / MEG Data EURASIP Journal on Applied Signal Processing, 4, (3), pp. 142-153.

More information

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

Catalogue record

Date deposited: 11 Dec 2003
Last modified: 18 Jul 2017 10:08

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Contributors

Author: S Weiss
Author: R M Leahy
Author: J C Mosher
Author: R W Stewart

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