Translation-invariant classification of non-stationary signals

Non-stationary signal classification is a complex problem. This problem becomes even more difficult if we add the following hypothesis: each signal includes a discriminant waveform, the time location of which is random and unknown. This is a problem that may arise in Brain Computer Interfaces (BCI) or in electroencephalogram recordings of patients prone to epilepsy. The aim of this article is to provide a new graph-based representation for classifying this kind of signals. This representation characterizes the waveform without reference to the absolute time location of the pattern in the signal. We will show that it is possible to create such a signal description using graphs on a time-scale or time–frequency signal representation. The definition of an inner product between graphs is then required to implement kernel methods algorithms like Support Vector Machines. Our experimental results shows that this approach is very promising and performs very well on real-world datasets.