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