Identifying timescales and possible precursors of the awake to asleep transition in EOG time series

Due to inherently high non-linearities involved, chaotic patterns were likely to occur in the experimental time series. These were analyzed therefore with the chaos theory. In particular we studied the time evolution of dynamical parameters as computed on different windows of the time series, i.e. optimal delay time as suggested by autocorrelation and mutual information on one side, embedding quality evaluation as suggested by the False Nearest Neighbours percentage on the other.