A machine learning approach to the analysis of timefrequency maps, and its application to neural dynamics
The statistical analysis of experimentally recorded brain activity patterns may require comparisons between large sets of complex signals in order to find meaningful similarities and differences between signals with large variability. High-level representations such as timefrequency maps convey a wealth of useful information, but they involve a large number of parameters that make statistical investigations of many signals difficult at present. In this paper, we describe a method that performs drastic reduction in the complexity of timefrequency representations through a modelling of the maps by elementary functions. The method is validated on artificial signals and subsequently applied to electrophysiological brain signals (local field potential) recorded from the olfactory bulb of rats while they are trained to recognize odours. From hundreds of experimental recordings, reproducible timefrequency events are detected, and relevant features are extracted, which allow further information processing, such as automatic classification.
machine learning timefrequency local field potential rat wavelet neural network bump olfaction odour recognition electro-encephalography