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Linear Spatial Integration for Single-Trial Detection in Encephalography
Parra, Lucas C.; Alvino, Chris; Tang, Akaysha; Pearlmutter, Barak A.; Yeung, Nick; Osman, Allen; Sajda, Paul
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.
Keyword(s): Computer Science; Hamilton Institute; Linear Spatial Integration; Single-Trial Detection; Encephalography
Publication Date:
Type: Journal article
Peer-Reviewed: Yes
Institution: Maynooth University
Citation(s): 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
Publisher(s): Elsevier
File Format(s): other
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First Indexed: 2020-01-31 06:15:05 Last Updated: 2020-04-02 07:00:44