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
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.
(Less)
- author
- Anderson, Rachele
LU
and Sandsten, Maria
LU
- organization
- publishing date
- 2017
- 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
- host publication
- 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
- 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
- conference location
- Tampere, Finland
- conference dates
- 2017-06-11 - 2017-06-15
- 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
- 2022-04-25 01:22:43
@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}},
keywords = {{EEG signals; Locally Stationary Processes; Memory Retrieval; Optimal spectral estimation; Time-frequency analysis}},
language = {{eng}},
pages = {{908--911}},
publisher = {{Springer}},
title = {{Stochastic modelling and optimal spectral estimation of EEG signals}},
url = {{http://dx.doi.org/10.1007/978-981-10-5122-7_227}},
doi = {{10.1007/978-981-10-5122-7_227}},
volume = {{65}},
year = {{2017}},
}