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

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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.

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Authors and Affiliations

  1. Department of Electrical Communication Engineering, Indian Institute of Science, 560 012, Bangalore, India

    V. Kavitha & D. Narayana Dutt

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  1. V. Kavitha
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  2. D. Narayana Dutt
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Correspondence to V. Kavitha.

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Kavitha, V., Narayana Dutt, D. A chaos-based model for low complexity predictive coding scheme for compression and transmission of electroencephalogram data. Med. Biol. Eng. Comput. 37, 316–321 (1999). https://doi.org/10.1007/BF02513306

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