DATA-COMPRESSION BY LINEAR PREDICTION FOR STORAGE AND TRANSMISSION OFEEG SIGNALS

The EEG time series has been subjected to various formalisms of analys is to extract meaningful information regarding the underlying neural e vents. In this paper the linear prediction (LP) method has been used f or analysis and presentation of spectral array data for the better vis ualisation of background EEG activity. It has also been used for signa l generation, efficient data storage and transmission of EEG. The LP m ethod is compared with the standard Fourier method of compressed spect ral array (CSA) of the multichannel EEG data. The autocorrelation auto regressive (AR) technique is used for obtaining the LP coefficients wi th 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 a nd 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 sto rage and transmission of EEG. The biological significance of Fourier m ethod and the LP method in respect to the microstructure of neuronal e vents in the generation of EEG is discussed.