Modelling of brain consciousness based on collaborative adaptive filters

Li, Ling and Xia, Yili and Jelfs, Beth and Cao, Jianting and Mandic, Danilo P. (2012) Modelling of brain consciousness based on collaborative adaptive filters. Neurocomputing, 76 (1). 36 - 43. ISSN 0925-2312. (doi:https://doi.org/10.1016/j.neucom.2011.05.038) (Access to this publication is currently restricted. You may be able to access a copy if URLs are provided)

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Abstract

A novel method for the discrimination between discrete states of brain consciousness is proposed, achieved through examination of nonlinear features within the electroencephalogram (EEG). To allow for real time modes of operation, a collaborative adaptive filtering architecture, using a convex combination of adaptive filters is implemented. The evolution of the mixing parameter within this structure is then used as an indication of the predominant nature of the {EEG} recordings. Simulations based upon a number of different filter combinations illustrate the suitability of this approach to differentiate between the coma and quasi-brain-death states based upon fundamental signal characteristics.

Item Type: Article
Additional information: Seventh International Symposium on Neural Networks (ISNN 2010)Advances in Web Intelligence
Uncontrolled keywords: Collaborative adaptive filtering; EEG; Quasi-brain-death (QBD); Coma; Signal nonlinearity
Subjects: Q Science
Q Science > QA Mathematics (inc Computing science) > QA 76 Software, computer programming, > QA76.87 Neural computers, neural networks
R Medicine
Divisions: Faculties > Sciences > School of Computing > Computational Intelligence Group
Faculties > Sciences > School of Computing > Data Science
Depositing User: Caroline Li
Date Deposited: 17 Jul 2015 16:24 UTC
Last Modified: 08 May 2018 08:53 UTC
Resource URI: https://kar.kent.ac.uk/id/eprint/49595 (The current URI for this page, for reference purposes)
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