Approximate time-variable coherence analysis of multichannel signals
(2002) In Multidimensional Systems and Signal Processing 13(3). p.237-264- Abstract
- We present a new method for signal extraction from noisy multichannel epileptic seizure onset EEG signals. These signals are non-stationary which makes time-invariant filtering unsuitable. The new method assumes a signal model and performs denoising by filtering the signal of each channel using a time-variable filter which is an estimate of the Wiener filter. The approximate Wiener filters are obtained using the time-frequency coherence functions between all channel pairs, and a fix-point algorithm. We estimate the coherence functions using the multiple window method, after which the fix-point algorithm is applied. Simulations indicate that this method improves upon its restriction to assumed stationary signals for realistically... (More)
- We present a new method for signal extraction from noisy multichannel epileptic seizure onset EEG signals. These signals are non-stationary which makes time-invariant filtering unsuitable. The new method assumes a signal model and performs denoising by filtering the signal of each channel using a time-variable filter which is an estimate of the Wiener filter. The approximate Wiener filters are obtained using the time-frequency coherence functions between all channel pairs, and a fix-point algorithm. We estimate the coherence functions using the multiple window method, after which the fix-point algorithm is applied. Simulations indicate that this method improves upon its restriction to assumed stationary signals for realistically non-stationary data, in terms of mean square error, and we show that it can also be used for time-frequency representation of noisy multichannel signals. The method was applied to two epileptic seizure onset signals, and it turned out that the most informative output of the method are the filters themselves studied in the time-frequency domain. They seem to reveal hidden features of the epileptic signal which are otherwise invisible. This algorithm can be used as preprocessing for seizure onset EEG signals prior to time-frequency representation and manual or algorithmic pattern classification. (Less)
Please use this url to cite or link to this publication:
https://lup.lub.lu.se/record/335463
- author
- Wahlberg, Patrik LU and Lantz, G
- organization
- publishing date
- 2002
- type
- Contribution to journal
- publication status
- published
- subject
- keywords
- time-frequency representation, Wiener filtering, denoising, signal extraction, EEG signal processing, epileptic seizure onsets, coherence, estimation, multiple window method
- in
- Multidimensional Systems and Signal Processing
- volume
- 13
- issue
- 3
- pages
- 237 - 264
- publisher
- Springer
- external identifiers
-
- wos:000176182000002
- scopus:0036646021
- ISSN
- 0923-6082
- DOI
- 10.1023/A:1015856312998
- language
- English
- LU publication?
- yes
- id
- 0e3171e0-6025-465a-8103-1cfcb0070f21 (old id 335463)
- date added to LUP
- 2016-04-01 15:18:04
- date last changed
- 2022-01-28 04:43:06
@article{0e3171e0-6025-465a-8103-1cfcb0070f21,
abstract = {{We present a new method for signal extraction from noisy multichannel epileptic seizure onset EEG signals. These signals are non-stationary which makes time-invariant filtering unsuitable. The new method assumes a signal model and performs denoising by filtering the signal of each channel using a time-variable filter which is an estimate of the Wiener filter. The approximate Wiener filters are obtained using the time-frequency coherence functions between all channel pairs, and a fix-point algorithm. We estimate the coherence functions using the multiple window method, after which the fix-point algorithm is applied. Simulations indicate that this method improves upon its restriction to assumed stationary signals for realistically non-stationary data, in terms of mean square error, and we show that it can also be used for time-frequency representation of noisy multichannel signals. The method was applied to two epileptic seizure onset signals, and it turned out that the most informative output of the method are the filters themselves studied in the time-frequency domain. They seem to reveal hidden features of the epileptic signal which are otherwise invisible. This algorithm can be used as preprocessing for seizure onset EEG signals prior to time-frequency representation and manual or algorithmic pattern classification.}},
author = {{Wahlberg, Patrik and Lantz, G}},
issn = {{0923-6082}},
keywords = {{time-frequency representation; Wiener filtering; denoising; signal extraction; EEG signal processing; epileptic seizure onsets; coherence; estimation; multiple window method}},
language = {{eng}},
number = {{3}},
pages = {{237--264}},
publisher = {{Springer}},
series = {{Multidimensional Systems and Signal Processing}},
title = {{Approximate time-variable coherence analysis of multichannel signals}},
url = {{http://dx.doi.org/10.1023/A:1015856312998}},
doi = {{10.1023/A:1015856312998}},
volume = {{13}},
year = {{2002}},
}