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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