Translation invariant classification of non-stationary signals
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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.
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