PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Implicit Wiener Series Analysis of Epileptic Seizure Recordings
A. Barbero Jimenez, Matthias Franz, W. Van Drongelen, J.R. Dorronsoro, Bernhard Schölkopf and Moritz Grosse-Wentrup
In: 31st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC 2009), 2-6 September 2009, Minneapolis, USA.

Abstract

Implicit Wiener series are a powerful tool to build Volterra representations of time series with any degree of nonlinearity. A natural question is then whether higher order representations yield more useful models. In this work we shall study this question for ECoG data channel relationships in epileptic seizure recordings, considering whether quadratic representations yield more accurate classifiers than linear ones. To do so we first show how to derive statistical information on the Volterra coefficient distribution and how to construct seizure classification patterns over that information. As our results illustrate, a quadratic model seems to provide no advantages over a linear one. Nevertheless, we shall also show that the interpretability of the implicit Wiener series provides insights into the inter-channel relationships of the recordings.

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EPrint Type:Conference or Workshop Item (Talk)
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Brain Computer Interfaces
ID Code:6153
Deposited By:Moritz Grosse-Wentrup
Deposited On:08 March 2010