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.
Restricted to Registered users only
Download (629Kb) | Request a copy
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.|
|Department/Centre:||Division of Electrical Sciences > Electrical Communication Engineering|
|Date Deposited:||09 Oct 2007|
|Last Modified:||19 Sep 2010 04:40|
Actions (login required)