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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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Contributor : Théodore Papadopoulo Connect in order to contact the contributor
Submitted on : Wednesday, July 31, 2013 - 4:12:39 PM
Last modification on : Friday, March 27, 2020 - 2:00:23 PM




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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