Journal
NEUROIMAGE
Volume 17, Issue 1, Pages 223-230Publisher
ACADEMIC PRESS INC ELSEVIER SCIENCE
DOI: 10.1006/nimg.2002.1212
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Funding
- NIMH NIH HHS [P50 MH62196] Funding Source: Medline
- NINDS NIH HHS [R01 NS37528] Funding Source: Medline
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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 A(z) approximate to 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. (C) 2002 Elsevier Science (USA).
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