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 measurement...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
1997.
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| Subjects: | |
| Online Access: | Get fulltext |
| LEADER | 01202 am a22001573u 4500 | ||
|---|---|---|---|
| 001 | 251912 | ||
| 042 | |a dc | ||
| 100 | 1 | 0 | |a Weiss, S. |e author |
| 700 | 1 | 0 | |a Leahy, R.M. |e author |
| 700 | 1 | 0 | |a Mosher, J.C. |e author |
| 700 | 1 | 0 | |a Stewart, R.W. |e author |
| 245 | 0 | 0 | |a An ensemble de-noising method for spatio-temporal EEG / MEG data |
| 260 | |c 1997. | ||
| 856 | |z Get fulltext |u https://eprints.soton.ac.uk/251912/1/weiss98d.ps | ||
| 520 | |a 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. | ||
| 655 | 7 | |a Article | |