[Partial autocorrelation function as a suitable description of basic EEG activity for use in classification procedures].

For the classification of stationary EEGs a discriminant approach is proposed which is based on the parameterisation of the EEG by the partial autocorrelation function. Regarding various criteria given in table 1, the parameterisation of the EEG by the partial autocorrelation function is compared to the parameterisation by the autocorrelation function, the autoregressive parameters, the power spectrum and band powers. The reliability of the autoregressive parameters, power spectra and band powers is reduced by empirical decisions made on model orders, degree of smoothing, and limits of frequency bands, respectively. The partial autocorrelation function does not show these drawbacks and, besides, it has optimal quantisation properties, especially if it is Fisher z-transformed. Therefore, the partial autocorrelation function is appropriate for data transmission between different computers and data exchange between EEG laboratories with a data reduction factor in the order of 10(2). There are several possibilities to interpret the partial autocorrelation function resulting from the relationship of the partial autocorrelation function and autoregressive models, though for the calculation of the partial autocorrelation function it is not necessary to assume that the EEG is a realisation of an autoregressive process of fixed order. The partial autocorrelation function immediately gives the error variance of the model fit and a value for the dynamic range of the power spectrum. Most commonly it is interpreted by the autoregressive power spectrum and spectral autoregressive parameters. For determining the adequate model order it is desirable to have pieces of EEG recordings which have a length of at least half a minute (table 2).(ABSTRACT TRUNCATED AT 250 WORDS)