Abstract
We present a robust, model-independent technique for measuring changes in the dynamics underlying nonlinear time-serial data. After constructing discrete density distributions of phase-space points on the attractor for time-windowed data sets, we measure the dissimilarity between density distributions via L 1-distance and χ 2 statistics. The discriminating power of the new measures is first tested on the Lorenz model and then applied to EEG data to detect the transition between non-seizure and epileptic activity. We find a clear superiority of the new measures in comparison to traditional nonlinear measures as discriminators of changing dynamics.
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