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

In: Modeling in Computational Biology and Biomedicine

Author

Listed:
  • Maureen Clerc

    (Athena project-team, Inria Sophia Antipolis Méditerranée)

  • Théodore Papadopoulo

    (Athena project-team, Inria Sophia Antipolis Méditerranée)

  • Christian Bénar

    (Aix-Marseille Université, Faculté de Médecine La Timone, Institut des Neurosciences des Systèmes -INS, UMR 1106 INSERM)

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.

Suggested Citation

  • Maureen Clerc & Théodore Papadopoulo & Christian Bénar, 2013. "Single-Trial Analysis of Bioelectromagnetic Signals: The Quest for Hidden Information," Springer Books, in: Frédéric Cazals & Pierre Kornprobst (ed.), Modeling in Computational Biology and Biomedicine, edition 127, chapter 0, pages 237-259, Springer.
    • Handle: RePEc:spr:sprchp:978-3-642-31208-3_7
    DOI: 10.1007/978-3-642-31208-3_7
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