Single-Trial Analysis of Bioelectromagnetic Signals: The Quest for Hidden Information

Abstract : This chapter deals with the analysis of multitrial electrophysiology datasets coming from neuroelectromagnetic recordings by electro-encephalography and magneto-encephalography (EEG and MEG). For such measurements, multitrial recordings are necessary in order to extract meaningful information. The obtained datasets present several characteristics: no ground-truth data, high level of noise (defined as the part of the data which is uncorrelated across trials), inter-trial variability. This chapter presents tools that deal with such datasets and their properties. The focus is on two families of data processing methods: data-driven methods, in a section on non-linear dimensionality reduction, and model-driven methods, in a section on Matching Pursuit and its extensions. The importance of correctly capturing the inter-trial variability is underlined in the last section which presents four case-studies in clinical and cognitive neuroscience.
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Chapitre d'ouvrage
Cazals, Frederic and Kornprobst, Pierre. Modeling in Computational Biology and Biomedicine: A Multidisciplinary Endeavor, Springer, pp.237--259, 2013, 978-3-642-31208-3. 〈10.1007/978-3-642-31208-3〉
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https://hal.inria.fr/hal-00849690
Contributeur : Théodore Papadopoulo <>
Soumis le : mercredi 31 juillet 2013 - 16:12:39
Dernière modification le : jeudi 18 janvier 2018 - 01:24:51

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Maureen Clerc, Théodore Papadopoulo, Christian Bénar. Single-Trial Analysis of Bioelectromagnetic Signals: The Quest for Hidden Information. Cazals, Frederic and Kornprobst, Pierre. Modeling in Computational Biology and Biomedicine: A Multidisciplinary Endeavor, Springer, pp.237--259, 2013, 978-3-642-31208-3. 〈10.1007/978-3-642-31208-3〉. 〈hal-00849690〉

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