A chaos-based model for low complexity predictive coding scheme for compression and transmission of electroencephalogram data
Medical and Biological Engineering and Computing, ISSN: 0140-0118, Vol: 37, Issue: 3, Page: 316-321
1999
- 4Citations
- 9Captures
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Article Description
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. 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.
Bibliographic Details
http://www.scopus.com/inward/record.url?partnerID=HzOxMe3b&scp=0032993491&origin=inward; http://dx.doi.org/10.1007/bf02513306; http://www.ncbi.nlm.nih.gov/pubmed/10505381; http://link.springer.com/10.1007/BF02513306; https://dx.doi.org/10.1007/bf02513306; https://link.springer.com/article/10.1007/BF02513306; http://www.springerlink.com/index/10.1007/BF02513306; http://www.springerlink.com/index/pdf/10.1007/BF02513306
Springer Science and Business Media LLC
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