Improved EEG Segmentation Using Non-linear Volterra Model in Bayesian Method
Abstract
In order to analyze non-stationary signals, like Electroencephalogram (EEG), it is sometimes easier to segment signals into pseudo-stationary segments. In this paper, the cascade of linear predictive coding (LPC) and non-linear Volterra filter is employed for modeling of noise in EEG signal and this methodology is applied to the procedure of change-point detection, for estimating the number of change-points and their exact location which is a...
Paper Details
Title
Improved EEG Segmentation Using Non-linear Volterra Model in Bayesian Method
Published Date
Nov 20, 2017
Journal
Volume
64
Issue
6
Pages
832 - 842
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