Stochastic modelling and optimal spectral estimation of EEG signals

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6 Citeringar (SciVal)

Sammanfattning

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.

Originalspråkengelska
Titel på värdpublikationEMBEC 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
FörlagSpringer
Sidor908-911
Antal sidor4
Volym65
ISBN (tryckt)9789811051210
DOI
StatusPublished - 2017
EvenemangJoint Conference of the European Medical and Biological Engineering Conference, EMBEC 2017 and Nordic-Baltic Conference on Biomedical Engineering and Medical Physics, NBC 2107 - Tampere, Finland
Varaktighet: 2017 juni 112017 juni 15

Konferens

KonferensJoint Conference of the European Medical and Biological Engineering Conference, EMBEC 2017 and Nordic-Baltic Conference on Biomedical Engineering and Medical Physics, NBC 2107
Land/TerritoriumFinland
OrtTampere
Period2017/06/112017/06/15

Ämnesklassifikation (UKÄ)

  • Radiologi och bildbehandling

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