Nonlinear Autoregressive Modelling

Characteristic nonlinearities of the 3/second ictal EEG identified by nonlinear autoregressive analysis

Nicholas D. Schiff, Jonathan D. Victor, Annemarie Canel, and Douglas R. Labar

Biological Cybernetics 72, 519-526 (1995)


We describe a method for the characterization of electroencephalographic signals based on a model which features nonlinear feedback. The characteristic EEG " fingerprints" obtained through this approach display the time-course of nonlinear interactions, rather than aspects susceptible to standard spectral analysis. Fingerprints of seizure discharges in six patients (five with typical absence seizures, one with complex partial seizures) revealed significant nonlinear interactions. The timing and pattern of these interactions correlated closely with the seizures type. NLAR analysis is compared with other nonlinear dynamical measures that have been applied to the EEG.

The " fingerprint" of a typical absence (3/second spike and wave) seizure

This map is obtained by the nonlinear autoregressive modelling method presented in this paper, and describes the nonlinear dynamics of this ictal discharge.

Background on nonlinear autoregressive analysis
Publication describing theoretical basis of approach
Related publication: absence seizures
Related publication: temporal lobe seizures
Publications related to clinical neurophysiology
Publications related to EEG analysis
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