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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.
