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Minimising prediction error for optimal nonlinear modelling of EEG signals using genetic algorithm

Balli, T., Palaniappan, Ramaswamy, Bhattacharya, J. (2009) Minimising prediction error for optimal nonlinear modelling of EEG signals using genetic algorithm. In: Proceedings of the 4th International IEEE/EMBS Conference on Neural Engineering. . pp. 363-366. IEEE ISBN 978-1-4244-2073-5. (doi:10.1109/NER.2009.5109308) (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/NER.2009.5109308

Abstract

Genetic algorithm (GA) is used for jointly estimating the embedding dimension and time lag parameters in order to achieve an optimal reconstruction of time series in state space. The conventional methods (false nearest neighbours and first minimum of the mutual information for estimating the embedding dimension and time lag, respectively) are also included for comparison purposes. The performance of GA and conventional parameters are tested by a one step ahead prediction modelling and estimation of dynamic invariants (i.e. approximate entropy). The results of this study indicated that the parameters selected by GA provide a better reconstruction (i.e. lower root mean square error) of EEG signals used for a Brain-Computer Interface (BCI) application. Additionally, GA based parameters are found to be computationally less intensive since both parameters are jointly optimised. In order to further illustrate the superiority of the embedding parameters estimated by GA, approximate entropy (ApEn) features using embedding parameters estimated by GA and conventional methods were computed. Next these ApEn features were used to classify the EEG signals into two classes (movement and non-movement) for BCI application. These results show that the embedding parameters estimated by GA are more appropriate than those estimated by the conventional methods for nonlinear modelling of EEG signals in state space.

Item Type: Conference or workshop item (Proceeding)
DOI/Identification number: 10.1109/NER.2009.5109308
Additional information: Unmapped bibliographic data: C7 - 5109308 [EPrints field already has value set] LA - English [Field not mapped to EPrints] J2 - Int. IEEE/EMBS Conf. Neural Eng., NER [Field not mapped to EPrints] AD - School of Computer Science and Electronic Engineering, University of Essex, Colchester, United Kingdom [Field not mapped to EPrints] AD - Department of Psychology, University of London, London, United Kingdom [Field not mapped to EPrints] AD - Goldsmiths College, Vienna, Austria [Field not mapped to EPrints] AD - Comission for Scientific Visualization, Austrian Academy of Sciences, Tech Gate, Vienna, Austria [Field not mapped to EPrints] DB - Scopus [Field not mapped to EPrints] M3 - Conference Paper [Field not mapped to EPrints] A4 - National Institutes of Health, NIH; National Institute of Neurological Disorders and Stroke, NINDS; National Science Foundation, NSF [Field not mapped to EPrints] C3 - 2009 4th International IEEE/EMBS Conference on Neural Engineering, NER '09 [Field not mapped to EPrints]
Uncontrolled keywords: Component, EEG, Embedding dimension, Genetic algorithm, Nonlinear prediction error, State space reconstruction, Time lag, Component, EEG, Embedding dimension, Nonlinear prediction error, State space reconstruction, Time lag, Electroencephalography, Genetic algorithms, Interfaces (computer), Repair, Time series, Parameter estimation
Divisions: Faculties > Sciences > School of Computing
Faculties > Sciences > School of Computing > Data Science
Depositing User: Palaniappan Ramaswamy
Date Deposited: 15 Dec 2018 17:37 UTC
Last Modified: 30 May 2019 08:29 UTC
Resource URI: https://kar.kent.ac.uk/id/eprint/70712 (The current URI for this page, for reference purposes)
Palaniappan, Ramaswamy: https://orcid.org/0000-0001-5296-8396
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