PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Translation invariant classification of non-stationary signals
Vincent Guigue, Alain Rakotomamonjy and Stéphane Canu
In: 13 th European Symposium on Artificial Neural Networks, 27-28-29 April 2005, Brugge, Belgium.

This is the latest version of this eprint.


Non-stationary signal classification is a difficult and complex problem. On top of that, we add the following hypothesis : each signal includes a discriminant waveform, the position of which is random and unknown. This is a problem that may arise in Brain Computer Interface (BCI). The aim of this article is to provide a new description to classify this kind of data. This representation must characterize the waveform without reference to the absolute time position of the pattern in the signal. We will show that it is possible to create a signal description using graphs on a time-scale representation. The definition of an inner product between graphs is then requested to implement classification algorithm. Our experimental results showed that this approach is very promising.

PDF - Requires Adobe Acrobat Reader or other PDF viewer.
EPrint Type:Conference or Workshop Item (Oral)
Project Keyword:Project Keyword UNSPECIFIED
Subjects:User Modelling for Computer Human Interaction
Learning/Statistics & Optimisation
Theory & Algorithms
ID Code:1923
Deposited By:Vincent Guigue
Deposited On:30 December 2005

Available Versions of this Item

  • Translation invariant classification of non-stationary signals (deposited 30 December 2005) [Currently Displayed]