Linear spatial integration for single-trial detection in encephalography. Parra, L., Alvino, C., Tang, A., Pearlmutter, B., Yeung, N., Osman, A., & Sajda, P. Neuroimage, 17(1):223–230, September, 2002. abstract bibtex 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 approximately 0.80 and faction 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 sensorymotor 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.
@article{ParraAlvinoTangEtAl02,
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 approximately 0.80 and faction 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 sensorymotor
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.},
added-at = {2008-09-16T23:39:07.000+0200},
author = {Parra, Lucas and Alvino, Chris and Tang, Akaysha and Pearlmutter, Barak and Yeung, Nick and Osman, Allen and Sajda, Paul},
biburl = {https://www.bibsonomy.org/bibtex/213c4bc3f3184ec2880375a5ef50003f8/brian.mingus},
description = {CCNLab BibTeX},
interhash = {9dc5afefdb18c665f785332973ed66f1},
intrahash = {13c4bc3f3184ec2880375a5ef50003f8},
journal = {Neuroimage},
keywords = {Humans; Laterality; Artificial Performance Intelligence; Motor Psychomotor Algorithms; Data Models; Interpretation, Movement; Activity; Magnetoencephalography; Linear Functional Imagination; Neurological; Electroencephalography; Models, Statistical;},
month = Sep,
number = 1,
owner = {frankmj},
pages = {223--230},
pmid = {12482079},
timestamp = {2008-09-16T23:40:53.000+0200},
title = {Linear spatial integration for single-trial detection in encephalography.},
volume = 17,
year = 2002
}
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