title: Non-linear and linear forecasting of the EEG time series.
abstract: The method of non-linear forecasting of time series was applied to different simulated signals and EEG in order to check its ability of distinguishing chaotic from noisy time series. The goodness of prediction was estimated, in terms of the correlation coefficient between forecasted and real time series, for non-linear and autoregressive (AR) methods. For the EEG signal both methods gave similar results. It seems that the EEG signal, in spite of its chaotic character, is well described by the AR model.
authors.affiliation: Laboratory of Medical Physics, Warsaw University, Poland.
authors.forename: K J
authors.initials: KJ
authors.lastname: Blinowska
authors.forename: M
authors.initials: M
authors.lastname: Malinowski
mesh_terms: D000465:Algorithms; D000818:Animals; D004569:Electroencephalography; D005544:Forecasting; D006801:Humans; D008433:Mathematics; D015233:Models, Statistical; D051381:Rats; D013995:Time