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EEG time series analysis with exponential autoregressive modelling

Balli, T., Palaniappan, Ramaswamy (2008) EEG time series analysis with exponential autoregressive modelling. In: 2008 Canadian Conference on Electrical and Computer Engineering. . pp. 485-488. IEEE ISBN 978-1-4244-1643-1. (doi:10.1109/CCECE.2008.4564581) (The full text of this publication is not currently available from this repository. You may be able to access a copy if URLs are provided)

The full text of this publication is not currently available from this repository. You may be able to access a copy if URLs are provided. (Contact us about this Publication)
Official URL
http://dx.doi.org/10.1109/CCECE.2008.4564581

Abstract

This paper proposes the use of exponential autoregressive (EAR) model for modelling of time series that are known to exhibit non-linear dynamics such as random fluctuations of amplitude and frequency. Biological signal (bio-signal) such as electroencephalogram (EEG) is known to exhibit nonlinear dynamics. Such signals cannot be modelled with traditional linear modelling techniques like autoregressive (AR) models as these models are known to provide only an approximation to the underlying properties of the non-linear signals. In this study, the suitability of EAR models as compared to AR models is shown using EEG signals in addition to several non-linear benchmark time series data where improved signal to noise ratio (SNR) values are indicated by the EAR models. Overall, the results indicate that use of EAR modelling which has yet to be exploited for bio-signal time series analysis has the huge potential in the characterisation and classification of EEG signals.

Item Type: Conference or workshop item (Proceeding)
DOI/Identification number: 10.1109/CCECE.2008.4564581
Additional information: Unmapped bibliographic data: C7 - 4564581 [EPrints field already has value set] LA - English [Field not mapped to EPrints] J2 - Can Conf Electr Comput Eng [Field not mapped to EPrints] AD - Department of Computing and Electronic Systems, University of Essex, Wivenhoe Park, CO4 3SQ, United Kingdom [Field not mapped to EPrints] DB - Scopus [Field not mapped to EPrints] M3 - Conference Paper [Field not mapped to EPrints] C3 - Canadian Conference on Electrical and Computer Engineering [Field not mapped to EPrints]
Uncontrolled keywords: Electroencephalogram, Exponential autoregressive, Genetic algorithm, Non-linear time series analysis, Argon, Dynamics, Electroencephalography, Signal processing, Signal to noise ratio, Technology, AR modeling, Auto-regressive, Autoregressive modeling, Autoregressive modelling, Bio signals, Biological signals, Characterisation, EEG signals, Electrical and computer engineering, Electroencephalogram, Electroencephalogram (EEG), Exponential autoregressive, Genetic algorithm, Modelling techniques, Non-linear, Non-linear dynamics, Non-linear time series analysis, Random fluctuations, Time-series, Time-series data, Time series analysis
Divisions: Faculties > Sciences > School of Computing
Faculties > Sciences > School of Computing > Data Science
Depositing User: Palaniappan Ramaswamy
Date Deposited: 15 Dec 2018 14:30 UTC
Last Modified: 30 May 2019 08:29 UTC
Resource URI: https://kar.kent.ac.uk/id/eprint/70715 (The current URI for this page, for reference purposes)
Palaniappan, Ramaswamy: https://orcid.org/0000-0001-5296-8396
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