EEG SPIKE AND WAVE MODELED BY A STOCHASTIC LIMIT-CYCLE

MANY reports based upon con-elation dimension studies have suggested t he chaotic nature of electroencephalographic spike and wave activity ( SW). Another study found no evidence for this, showing that surrogate stochastic data generated from SW have the same dynamical properties a s the original data. The present paper explicitly models SW as the out put of a non-linear stochastic system, Non-linear non-parametric kerne l autoregression is used to show that the attractor of this system may be a limit cycle. The addition of system noise originates a simulated time series with the same interspike interval variability originally hypothesized as chaotic. Tests performed, on the correlation dimension obtained from the original data as well as from both linear and non-l inear surrogates show that the noise-perturbed limit cycle model achie ves the best fit to the original data, We conclude that SW is likely t o be a form of stochastic disturbed limit cycle behaviour rather than chaos.