TY - GEN

T1 - Measuring neural synchrony by message passing

AU - Dauwels, Justin

AU - Vialatte, François

AU - Rutkowski, Tomasz

AU - Cichocki, Andrzej

PY - 2009

Y1 - 2009

N2 - 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, variance of the timing jitter, fraction of "spurious" events, and average similarity of events. SES may be applied to generic one-dimensional and multi-dimensional point processes, however, the paper mainly focusses on point processes in time-frequency domain. The average event similarity is in that case described by two parameters: the average frequency offset between events in the time-frequency plane, and the variance of the frequency offset ("frequency jitter"); SES then consists of five parameters in total. Those parameters quantify the synchrony of oscillatory events, and hence, they provide an alternative to existing synchrony measures that quantify amplitude or phase synchrony. The pairwise alignment of point processes is cast as a statistical inference problem, which is solved by applying the maxproduct algorithmon a graphicalmodel. 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; the results indicate that SES significantly improves the sensitivity of EEG in detecting MCI.

AB - 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, variance of the timing jitter, fraction of "spurious" events, and average similarity of events. SES may be applied to generic one-dimensional and multi-dimensional point processes, however, the paper mainly focusses on point processes in time-frequency domain. The average event similarity is in that case described by two parameters: the average frequency offset between events in the time-frequency plane, and the variance of the frequency offset ("frequency jitter"); SES then consists of five parameters in total. Those parameters quantify the synchrony of oscillatory events, and hence, they provide an alternative to existing synchrony measures that quantify amplitude or phase synchrony. The pairwise alignment of point processes is cast as a statistical inference problem, which is solved by applying the maxproduct algorithmon a graphicalmodel. 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; the results indicate that SES significantly improves the sensitivity of EEG in detecting MCI.

UR - http://www.scopus.com/inward/record.url?scp=84858786175&partnerID=8YFLogxK

M3 - Conference contribution

AN - SCOPUS:84858786175

SN - 160560352X

SN - 9781605603520

T3 - Advances in Neural Information Processing Systems 20 - Proceedings of the 2007 Conference

BT - Advances in Neural Information Processing Systems 20 - Proceedings of the 2007 Conference

T2 - 21st Annual Conference on Neural Information Processing Systems, NIPS 2007

Y2 - 3 December 2007 through 6 December 2007

ER -