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    Linear Spatial Integration for Single-Trial Detection in Encephalography


    Parra, Lucas C. and Alvino, Chris and Tang, Akaysha and Pearlmutter, Barak A. and Yeung, Nick and Osman, Allen and Sajda, Paul (2002) Linear Spatial Integration for Single-Trial Detection in Encephalography. NeuroImage, 17 (1). pp. 223-230. ISSN 1053-8119

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    Abstract

    Conventional analysis of electroencephalography (EEG) and magnetoencephalography (MEG) often relies on averaging over multiple trials to extract statistically relevant differences between two or more experimental conditions. In this article we demonstrate single-trial detection by linearly integrating information over multiple spatially distributed sensors within a predefined time window. We report an average, single- trial discrimination performance of Az � 0.80 and fraction correct between 0.70 and 0.80, across three distinct encephalographic data sets. We restrict our approach to linear integration, as it allows the computation of a spatial distribution of the discriminating component activity. In the present set of experiments the resulting component activity distributions are shown to correspond to the functional neuroanatomy consistent with the task (e.g., contralateral sensory– motor cortex and anterior cingulate). Our work demonstrates how a purely data-driven method for learning an optimal spatial weighting of encephalographic activity can be validated against the functional neuroanatomy.

    Item Type: Article
    Keywords: Linear Spatial Integration; Single-Trial Detection; Encephalography;
    Academic Unit: Faculty of Science and Engineering > Computer Science
    Faculty of Science and Engineering > Research Institutes > Hamilton Institute
    Item ID: 5503
    Identification Number: https://doi.org/10.1006/nimg.2002.1212
    Depositing User: Barak Pearlmutter
    Date Deposited: 15 Oct 2014 11:16
    Journal or Publication Title: NeuroImage
    Publisher: Elsevier
    Refereed: Yes
    URI:

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