Hi, Don't forget tomorrow's MITACS-Lisa-McGill seminar!

Title: Analysis of EEG data by means of ordinal pattern distributions Speaker: Mathieu Sinn Date: Friday, November 21st, 15:00 Place: Room 3195 (pav. André-Aisenstadt, UdeM)

Abstract: Ordinal time series analysis is a new fast and robust approach to the investigation of long and complex time series, such as electroencephalographic (EEG) data. The basic idea is to consider locally the order relations between the values instead of the values themselves.

An ordinal pattern represents the order relations between a finite number of equidistant values of a time series. Assuming that these values are pairwise different, their unique rank order can be simply described by a permutation. By looking at the distributions of ordinal patterns (or measures derived from the distributions) in different parts of a time series, changes in the dynamic of the underlying system can be detected, such as increasing or decreasing complexity. One derived measure is the permutation entropy, which is the Shannon entropy of ordinal pattern distributions. It has been shown that in certain cases of time series obtained by dynamical systems, the permutation entropy is related to the Kolmogorov-Sinai entropy which is a well-known measure for the complexity of time series and the underlying systems, respectively.

A main application of the permutation entropy so far has been the detection of epileptic activity in EEG data, as it is widely accepted that the complexity of the brain dynamics changes (namely, decreases) during epileptic seizures. However, since clearly different ordinal pattern distributions may yield very similar permutation entropy values, we propose to take the whole distributions into account. First attempts show that a cluster analysis of ordinal pattern distributions may be a good approach to an automatic separation and classification of EEG data. Beneath the detection and localization of epileptic activity, another interesting application of this method is the automatic classification of sleep stages according to the criterions of Rechtschaffen and Kales.

This is joint work with Karsten Keller.

-- Pascal