Stochastic modelling and optimal spectral estimation of EEG signals
(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.
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- 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}}, }