Jitter-Adaptive Dictionary Learning - Application to Multi-Trial Neuroelectric Signals

Abstract : The simultaneous analysis of multiple recordings of neuronal electromagnetic activity is an important task requiring sophisticated and extremely noise robust techniques. A general goal is to find a representation of the similarities (e.g. repeating waveforms) as well as the differences (e.g. varying shape, latency, phase, or amplitude of waveforms) across the signals. Here, we present an extension to dictionary learning that explicitly accounts for small variations in latency and phase of atoms.
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Poster communications
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https://hal.inria.fr/hal-01094663
Contributor : Sebastian Hitziger <>
Submitted on : Monday, January 5, 2015 - 6:08:01 PM
Last modification on : Thursday, October 17, 2019 - 12:36:09 PM
Long-term archiving on: Monday, April 6, 2015 - 10:06:36 AM

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  • HAL Id : hal-01094663, version 1

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Sebastian Hitziger, Maureen Clerc, Alexandre Gramfort, Sandrine Saillet, Christian Bénar, et al.. Jitter-Adaptive Dictionary Learning - Application to Multi-Trial Neuroelectric Signals. International Conference on Learning Representations 2013, May 2013, Phoenix, Arizona, United States. 2013. ⟨hal-01094663⟩

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