Fitting autoregressive models to EEG time series: An empirical comparison of estimates of the order
H.-W. Steinberg, Thomas Gasser, Jürgen Franke · IEEE Transactions on Acoustics Speech and Signal Processing · 1985
A parametrization of the spectral structure of time series data is of great interest in many fields of science such as eleetroencephalography (EEG). Autoregressive processes offer such a representation. The problem is to find a parsimonious representation which fits the data well. Three criteria for determining the order of autoregressive models are, therefore, compared. A simulation is done for studying the consistency of these criteria. A goodness-of-fit test derived from the runs test judges the adequacy of the autoregressive representation of the spectral structure of EEG data. For neurophysiological and statistical reasons, autoregressive modeling in a restricted frequency domain is introduced.