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A chaos-based model for low complexity predictive coding scheme for compression and transmission of electroencephalogram data

Kavitha, V and Dutt, Narayana D (1999) A chaos-based model for low complexity predictive coding scheme for compression and transmission of electroencephalogram data. In: Medical and Biological Engineering and Computing, 37 (1). pp. 316-321.

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Abstract

A method for low complexity, low bit rate transmission of EEG (electroencephalogram) data, based on chaotic principles, is presented. The EEG data is assumed to be generated by a non-linear dynamical system of E dimensions. The E dynamical variables are reconstructed from the one-dimensional time series by the process of time-delay embedding. A model of the form X[n+1]=F(X[n], X[n−1],...X[n−p]) is fitted for the data in the E-dimensional space and this model is used as predictor in the predictive coding scheme for transmission. This model is able to give a reduction of nearly 50% of the dynamic range of the error signal to be transmitted, with a reduced complexity, when compared to the conventionally used linear prediction method. This implies that a reduced bit rate of transmission with a reduced complexity can be obtained. The effects of variation of model parameters on the complexity and bit rate are discussed.

Item Type: Journal Article
Additional Information: Copyright of this article belongs to Springer.
Keywords: Electroencephalogram(EEG);Chaos;Modelling;Transmission; Prediction
Department/Centre: Division of Electrical Sciences > Electrical Communication Engineering
Date Deposited: 09 Oct 2007
Last Modified: 19 Sep 2010 04:40
URI: http://eprints.iisc.ernet.in/id/eprint/12143

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