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 powerful way to detect the change-points as precisely as possible. The earlier results are completed by constructing algorithms that use the cascade of LPC and non-linear Volterra filter for modeling the relation between noisy signal and noise in practical situations. In a Bayesian configuration, the posterior distribution of the change-point sequence is constructed and then Markov Chain Monte Carlo procedure is used for sampling this posterior distribution. The simulation results for segmentation of synthetic and real EEG data show that by applying our newly proposed methodology, the specificity and sensitivity of the segmentation are highly improved. In the case of synthetic data, the change-points are estimated completely precise (100% correct) in 70% of times and they are estimated with at least 98% accuracy in other times.

KEYWORDS:

• Bayesian method
• EEG segmentation
• LPC
• Volterra filter

DISCLOSURE STATEMENT

No potential conflict of interest was reported by the authors.

Additional information

Notes on contributors

Malihe Hassani

Maliheh Hassani has received her BSc and MSc degrees in electrical and electronic engineering in 2002 and 2005, respectively. Now she is a PhD candidate in Babol Noshirvani Institute of Technology, Department of Electrical and Electronic Engineering; She published more than seven articles in journals and conferences. Her research interests are digital signal processing, biomedical signal processing, and image.

E-mail: [email protected]

Mohammad-Reza Karami

Mohammad-Reza Karami has received the BSc degree in electrical and electronic engineering in 1992, MSc degree of signal processing in 1994, and PhD degree in 1998 in biomedical engineering from I.N.P.L. d'Nancy of France. He is now the Associate Professor with the Department of Electrical and Computer Engineering, Babol University of Technology. Since 1998, his research is in signal and speech processing. He published more than 100 articles in journals and conferences. He teaches digital signal processing, biomedical signal processing and speech processing in university. His research interests include speech, image, and signal.

E-mail: [email protected]