title: A machine learning approach to the analysis of time-frequency maps, and its application to neural dynamics.
abstract: 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 time-frequency 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 time-frequency 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 time-frequency events are detected, and relevant features are extracted, which allow further information processing, such as automatic classification.
authors.affiliation: Ecole Supérieure de Physique et de Chimie Industrielles de la Ville de Paris (ESPCI-Paristech), Laboratoire d'Electronique (UMR 7084 CNRS), 10 rue Vauquelin, 75005 Paris, France.
authors.forename: François B
authors.initials: FB
authors.lastname: Vialatte
authors.forename: Claire
authors.initials: C
authors.lastname: Martin
authors.forename: Rémi
authors.initials: R
authors.lastname: Dubois
authors.forename: Joëlle
authors.initials: J
authors.lastname: Haddad
authors.forename: Brigitte
authors.initials: B
authors.lastname: Quenet
authors.forename: Rémi
authors.initials: R
authors.lastname: Gervais
authors.forename: Gérard
authors.initials: G
authors.lastname: Dreyfus
mesh_terms: D000465:Algorithms; D000818:Animals; D001185:Artificial Intelligence; D001921:Brain / Q000166:cytology / Q000502:physiology; D001931:Brain Mapping; D002755:Choice Behavior / Q000502:physiology; D004569:Electroencephalography / Q000379:methods; D005583:Fourier Analysis; D006801:Humans; D016571:Neural Networks, Computer; D009474:Neurons / Q000502:physiology*; D017711:Nonlinear Dynamics; D012815:Signal Processing, Computer-Assisted; D013997:Time Factors