Robust detection of dynamical change in scalp EEG

We present a robust, model-independent technique for measuring changes in the dynamics underlying nonlinear time-serial data. We define indicators of dynamical change by comparing distribution functions on the attractor via LI-distance and chi (2) statistics. We apply the measures to scalp EEG data with the objective of capturing the transition between non-seizure and epileptic brain activity in a timely, accurate, and non-invasive mariner. We find a clear superiority of the new metrics in comparison to traditional nonlinear measures as discriminators of dynamical change.