Event synchronization: a simple and fast method to measure synchronicity and time delay patterns.

By R. Q. Quiroga, Thomas Kreuz, Peter Grassberger. Physical review. E, Statistical, nonlinear, and soft matter physics, 2002

430 citations recorded by the source corpus.

We propose a simple method to measure synchronization and time-delay patterns between signals. It is based on the relative timings of events in the time series, defined, e.g., as local maxima. The degree of synchronization is obtained from the number of quasisimultaneous appearances of events, and the delay is calculated from the precedence of events in one signal with respect to the other. Moreover, we can easily visualize the time evolution of the delay and synchronization level with an excellent resolution. We apply the algorithm to short rat electroencephalogram (EEG) signals, some of them containing spikes. We also apply it to an intracranial human EEG recording containing an epileptic seizure, and we propose that the method might be useful for the detection of epileptic foci. It can be easily extended to other types of data and it is very simple and fast, thus being suitable for on-line implementations.

Subfields: Cognitive Neuroscience, Computer Networks and Communications

View Paper: https://www.semanticscholar.org/paper/2c513b54e3f3d67e715c1806541a11a8e3c6934d

Citations in the corpus

• Similarity estimators for irregular and age-uncertain time series (2013, neutral): “Thus, the delayτ cannot be allowed to be arbitrarily large or small, as inMalik et al. (2010) or Quian Quiroga et al. (2002).”