Nonlinear Autoregressive Modelling

Nonlinear autoregressive analysis of the 3/second ictal EEG: implications for underlying dynamics

Nicholas D. Schiff, Jonathan D. Victor, and Annemarie Canel

Biological Cybernetics 72, 527-532 (1995)


In a previous study (Schiff et al. 1991, Schiff et al. 1995) nonlinear autoregressive (NLAR) models applied to ictal EEG recordings in six patients revealed nonlinear signal interactions that correlated with seizure type and clinical diagnosis. Here we interpret these models from a theoretical viewpoint. Extended models with multiple nonlinear terms are employed to demonstrate independence of nonlinear dynamical interactions identified in the "NLAR fingerprint" of patients with 3/second seizure discharges. Analysis of the role of periodicity in the EEG signal reveals that the fingerprints reflect the dynamics not only of the periodic discharge itself, but also of the fluctuations of each cycle about an average waveform. A stability analysis is used to make qualitative inferences concerning network properties of the ictal generators. Finally, the NLAR fingerprint is analyzed in the context of Volterra-Weiner theory.

Background on nonlinear autoregressive analysis
Publication describing theoretical basis of approach
Related publication: absence seizures
Related publication: temporal lobe seizures
Related publication: hierarchical decomposition of multivariate time series
Publications related to EEG analysis
Return to publications list