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Stochastic modelling and optimal spectral estimation of EEG signals

Anderson, Rachele LU and Sandsten, Maria LU (2017) Joint Conference of the European Medical and Biological Engineering Conference, EMBEC 2017 and Nordic-Baltic Conference on Biomedical Engineering and Medical Physics, NBC 2107 In EMBEC and NBC 2017 - Joint Conference of the European Medical and Biological Engineering Conference EMBEC 2017 and the Nordic-Baltic Conference on Biomedical Engineering and Medical Physics, NBC 2017 65. p.908-911
Abstract

The study of a time-frequency image is often the method of choice to address key issues in cognitive electrophysiology. The quality of the time-frequency representation is crucial for the extraction of robust and relevant features, thus leading to the demand for highly performing spectral estimators. We consider a stochastic model, known as Locally Stationary Processes, based on the modulation in time of a stationary covariance function. The flexibility of the model makes it suitable for a wide range of time-varying signals, in particular EEG signals. Previous works provided the theoretical expression of the mean-square error optimal kernel for the computation of the Wigner-Ville spectrum. The introduction of a novel inference method... (More)

The study of a time-frequency image is often the method of choice to address key issues in cognitive electrophysiology. The quality of the time-frequency representation is crucial for the extraction of robust and relevant features, thus leading to the demand for highly performing spectral estimators. We consider a stochastic model, known as Locally Stationary Processes, based on the modulation in time of a stationary covariance function. The flexibility of the model makes it suitable for a wide range of time-varying signals, in particular EEG signals. Previous works provided the theoretical expression of the mean-square error optimal kernel for the computation of the Wigner-Ville spectrum. The introduction of a novel inference method for the model parameters permits the computation of the optimal kernel in real-world data cases. The obtained MSE optimal time-frequency estimator is compared with other commonly used methods in a simulation study, confirming the error reduction. Optimal spectral estimates are presented for the case study, consisting of EEG data collected within a research on memory retrieval.

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Please use this url to cite or link to this publication:
author
organization
publishing date
type
Chapter in Book/Report/Conference proceeding
publication status
published
subject
keywords
EEG signals, Locally Stationary Processes, Memory Retrieval, Optimal spectral estimation, Time-frequency analysis
in
EMBEC and NBC 2017 - Joint Conference of the European Medical and Biological Engineering Conference EMBEC 2017 and the Nordic-Baltic Conference on Biomedical Engineering and Medical Physics, NBC 2017
volume
65
pages
4 pages
publisher
Springer Verlag
conference name
Joint Conference of the European Medical and Biological Engineering Conference, EMBEC 2017 and Nordic-Baltic Conference on Biomedical Engineering and Medical Physics, NBC 2107
external identifiers
  • scopus:85021714784
ISBN
9789811051210
DOI
10.1007/978-981-10-5122-7_227
language
English
LU publication?
yes
id
51c6a8fb-7553-4df9-b58a-a8223d469cc2
date added to LUP
2017-07-18 10:22:37
date last changed
2018-01-07 12:12:00
@inproceedings{51c6a8fb-7553-4df9-b58a-a8223d469cc2,
  abstract     = {<p>The study of a time-frequency image is often the method of choice to address key issues in cognitive electrophysiology. The quality of the time-frequency representation is crucial for the extraction of robust and relevant features, thus leading to the demand for highly performing spectral estimators. We consider a stochastic model, known as Locally Stationary Processes, based on the modulation in time of a stationary covariance function. The flexibility of the model makes it suitable for a wide range of time-varying signals, in particular EEG signals. Previous works provided the theoretical expression of the mean-square error optimal kernel for the computation of the Wigner-Ville spectrum. The introduction of a novel inference method for the model parameters permits the computation of the optimal kernel in real-world data cases. The obtained MSE optimal time-frequency estimator is compared with other commonly used methods in a simulation study, confirming the error reduction. Optimal spectral estimates are presented for the case study, consisting of EEG data collected within a research on memory retrieval.</p>},
  author       = {Anderson, Rachele and Sandsten, Maria},
  booktitle    = {EMBEC and NBC 2017 - Joint Conference of the European Medical and Biological Engineering Conference EMBEC 2017 and the Nordic-Baltic Conference on Biomedical Engineering and Medical Physics, NBC 2017},
  isbn         = {9789811051210},
  keyword      = {EEG signals,Locally Stationary Processes,Memory Retrieval,Optimal spectral estimation,Time-frequency analysis},
  language     = {eng},
  pages        = {908--911},
  publisher    = {Springer Verlag},
  title        = {Stochastic modelling and optimal spectral estimation of EEG signals},
  url          = {http://dx.doi.org/10.1007/978-981-10-5122-7_227},
  volume       = {65},
  year         = {2017},
}