Compression algorithm as a tool for EEG data processing

The paper presents a new methodology of finding and estimating main features of time series to achieve reduction of their components and thus providing the compression of information contained in it keeping the selected features invariant. The presented compression algorithm is based on estimation of truncated time series components in such a way that the spectrum functions of both original and truncated time series are sufficiently close together. In the end, the set of examples is shown to demonstrate the algorithm performance and to indicate the applications of the presented methodology on EEG (Electroencephalography) signals.