A novel approach to measure the interdependence of two time series is proposed, referred to as “stochastic event synchrony” (SES); it quantifies the alignment of two point processes by means of the following parameters: time delay, standard deviation of the timing jitter, the fraction of “spurious” events, and the average similarity of the events. In contrast to the other measures, SES quantifies the synchrony of oscillatory events (instead of more conventional amplitude or phase synchrony). Pairwise alignment of the point processes is cast as a statistical inference problem, which is solved by applying the max-product algorithm on a graphical model. The SES parameters are determined from the resulting pairwise alignment by maximum a posteriori (MAP) estimation. The proposed interdependence measure is applied to the problem of detecting anomalies in EEG synchrony of Mild Cognitive Impairment (MCI) patients.