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Time-variable filtering of multichannel signals using multiple windows coherence and the Weyl transform

Wahlberg, Patrik LU and Sandsten, Maria LU (2000) 2000 IEEE Sensor Array and Multichannel Signal Processing Workshop
Abstract
This paper deals with noise suppression in multichannel measurements, using a certain signal model but without assuming stationarity of the signals involved. This enables application to signals whose spectral characteristics are time-variable. The novelty consists of an algorithm for obtaining an approximation of the Wiener filter for each channel. The filters are computed using the Weyl transform and estimates of the time-frequency coherence function between all channel pairs. Time-frequency coherence functions are estimated using the multiple window method, adapted to peaked spectra. Our method is evaluated on EEG signals from epileptic seizure onsets which are measured at multiple locations on the scalp. The filtered signals give... (More)
This paper deals with noise suppression in multichannel measurements, using a certain signal model but without assuming stationarity of the signals involved. This enables application to signals whose spectral characteristics are time-variable. The novelty consists of an algorithm for obtaining an approximation of the Wiener filter for each channel. The filters are computed using the Weyl transform and estimates of the time-frequency coherence function between all channel pairs. Time-frequency coherence functions are estimated using the multiple window method, adapted to peaked spectra. Our method is evaluated on EEG signals from epileptic seizure onsets which are measured at multiple locations on the scalp. The filtered signals give improved time-frequency representations, and also the resulting filters studied in the time-frequency domain reveal otherwise not visible signal features. (Less)
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author
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organization
publishing date
type
Chapter in Book/Report/Conference proceeding
publication status
published
subject
host publication
Proceedings of the 2000 IEEE Sensor Array and Multichannel Signal Processing Workshop. SAM 2000
publisher
IEEE - Institute of Electrical and Electronics Engineers Inc.
conference name
2000 IEEE Sensor Array and Multichannel Signal Processing Workshop
conference location
Cambridge, MA, United States
conference dates
2000-03-17 - 2000-03-17
external identifiers
  • scopus:79960790755
ISBN
0-7803-6339-6
DOI
10.1109/SAM.2000.878038
language
English
LU publication?
yes
id
c81cea49-f474-4b71-be7a-bb0255bb3eb6
date added to LUP
2018-03-23 16:09:47
date last changed
2021-01-04 13:27:14
@inproceedings{c81cea49-f474-4b71-be7a-bb0255bb3eb6,
  abstract     = {This paper deals with noise suppression in multichannel measurements, using a certain signal model but without assuming stationarity of the signals involved. This enables application to signals whose spectral characteristics are time-variable. The novelty consists of an algorithm for obtaining an approximation of the Wiener filter for each channel. The filters are computed using the Weyl transform and estimates of the time-frequency coherence function between all channel pairs. Time-frequency coherence functions are estimated using the multiple window method, adapted to peaked spectra. Our method is evaluated on EEG signals from epileptic seizure onsets which are measured at multiple locations on the scalp. The filtered signals give improved time-frequency representations, and also the resulting filters studied in the time-frequency domain reveal otherwise not visible signal features.},
  author       = {Wahlberg, Patrik and Sandsten, Maria},
  booktitle    = {Proceedings of the 2000 IEEE Sensor Array and Multichannel Signal Processing Workshop. SAM 2000},
  isbn         = {0-7803-6339-6},
  language     = {eng},
  publisher    = {IEEE - Institute of Electrical and Electronics Engineers Inc.},
  title        = {Time-variable filtering of multichannel signals using multiple windows coherence and the Weyl transform},
  url          = {http://dx.doi.org/10.1109/SAM.2000.878038},
  doi          = {10.1109/SAM.2000.878038},
  year         = {2000},
}