Advanced

Approximate time-variable coherence analysis of multichannel signals

Wahlberg, Patrik LU and Lantz, G (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:
author
organization
publishing date
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
Kluwer
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
2007-11-19 14:41:09
date last changed
2017-01-01 06:37:04
@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},
  keyword      = {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    = {Kluwer},
  series       = {Multidimensional Systems and Signal Processing},
  title        = {Approximate time-variable coherence analysis of multichannel signals},
  url          = {http://dx.doi.org/10.1023/A:1015856312998},
  volume       = {13},
  year         = {2002},
}