Modelling of brain consciousness based on collaborative adaptive filters

Authors:
Ling Li;Yili Xia;Beth Jelfs;Jianting Cao;Danilo P. Mandic
Affiliations:
Department of Electrical and Electronic Engineering, Imperial College London, SW7 2BT, UK;Department of Electrical and Electronic Engineering, Imperial College London, SW7 2BT, UK;Department of Electrical and Electronic Engineering, Imperial College London, SW7 2BT, UK;Department of Electronic Engineering, Saitama Institute of Technology, Japan and Laboratory for Advanced Brain Signal Processing, RIKEN Brain Science Institute, Saitama 351-0198, Japan and East Ch ...;Department of Electrical and Electronic Engineering, Imperial College London, SW7 2BT, UK
Venue:
Neurocomputing
Year:
2012

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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.