A Theory for Multiresolution Signal Decomposition: The Wavelet Representation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Numerical recipes in C (2nd ed.): the art of scientific computing
Numerical recipes in C (2nd ed.): the art of scientific computing
The nature of statistical learning theory
The nature of statistical learning theory
Complex independent component analysis of frequency-domain electroencephalographic data
Neural Networks - Special issue: Neuroinformatics
ICANN'05 Proceedings of the 15th international conference on Artificial Neural Networks: biological Inspirations - Volume Part I
Matching pursuits with time-frequency dictionaries
IEEE Transactions on Signal Processing
Computer Methods and Programs in Biomedicine
Neural Information Processing
Improved sparse bump modeling for electrophysiological data
ICONIP'08 Proceedings of the 15th international conference on Advances in neuro-information processing - Volume Part I
An exemplar-based statistical model for the dynamics of neural synchrony
ICONIP'08 Proceedings of the 15th international conference on Advances in neuro-information processing - Volume Part I
On the synchrony of morphological and molecular signaling events in cell migration
ICONIP'08 Proceedings of the 15th international conference on Advances in neuro-information processing - Volume Part I
ICONIP'06 Proceedings of the 13th international conference on Neural information processing - Volume Part III
ICANN'05 Proceedings of the 15th international conference on Artificial Neural Networks: biological Inspirations - Volume Part I
Quantifying statistical interdependence, part iii: N 2 point processes
Neural Computation
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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.