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Data compression by linear prediction for storage and transmission of EEG signals

Pradhan, N and Dutt, DN (1994) Data compression by linear prediction for storage and transmission of EEG signals. In: International journal of bio-medical computing, 35 (3). pp. 207-217.

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Official URL: http://dx.doi.org/10.1016/0020-7101(94)90076-0


The EEG time series has been subjected to various formalisms of analysis to extract meaningful information regarding the underlying neural events. In this paper the linear prediction (LP) method has been used for analysis and presentation of spectral array data for the better visualisation of background EEG activity. It has also been used for signal generation, efficient data storage and transmission of EEG. The LP method is compared with the standard Fourier method of compressed spectral array (CSA) of the multichannel EEG data. The autocorrelation autoregressive (AR) technique is used for obtaining the LP coefficients with a model order of 15. While the Fourier method reduces the data only by half, the LP method just requires the storage of signal variance and LP coefficients. The signal generated using white Gaussian noise as the input to the LP filter has a high correlation coefficient of 0.97 with that of original signal, thus making LP as a useful tool for storage and transmission of EEG. The biological significance of Fourier method and the LP method in respect to the microstructure of neuronal events in the generation of EEG is discussed.

Item Type: Journal Article
Publication: International journal of bio-medical computing
Publisher: Elsevier science
Additional Information: Copyright of this article belongs to Elsevier science.
Department/Centre: Division of Electrical Sciences > Electrical Communication Engineering
Date Deposited: 02 Jun 2011 09:06
Last Modified: 02 Jun 2011 09:06
URI: http://eprints.iisc.ac.in/id/eprint/36393

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