Autoregression models of EEG
โ Scribed by J. J. Wright; R. R. Kydd; A. A. Sergejew
- Book ID
- 104659983
- Publisher
- Springer-Verlag
- Year
- 1990
- Tongue
- English
- Weight
- 844 KB
- Volume
- 62
- Category
- Article
- ISSN
- 0340-1200
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โฆ Synopsis
This paper considers the properties of parameters (natural frequencies and damping coefficients) obtained from segment-by-segment autoregression analysis of ECoG of rat. The use of a reference signal as control for parameter estimate errors, and multiple regression analyses indicate that the dependencies among parameters calculated from ECoG in the alert (desynchronised) state are of a form consistent with imposition of time-invariance assumptions (implicit in autoregression) on an inherently non-stationary, multimodal, linear and near-equilibrium "thermal" process.
๐ SIMILAR VOLUMES
The autoregressive (AR) model is a widely used tool in electroencephalogram (EEG) analysis. The dependence of the AR model on both the segment length and several characteristic EEG patterns is addressed. The best AR model order is computed with three different criteria. The results show that the Ris