Decomposing spatiotemporal brain patterns into topographic latent sources

NeuroImage

Volumn 98, Issue , 2014, Pages 91-102

  Gershman, Samuel J ^a   Blei, David M ^b   Norman, Kenneth A ^c   Sederberg, Per B ^d  

^a MASSACHUSETTS INSTITUTE OF TECHNOLOGY   (United States)

^b Princeton University   (United States)

^c PRINCETON UNIVERSITY   (United States)

^d OHIO STATE UNIVERSITY   (United States)

Author keywords

Bayesian; Decoding; FMRI; Multivariate; Variational

Indexed keywords

ACCURACY; ALGORITHM; ARTICLE; BAYES THEOREM; COMPUTER PREDICTION; CONTROLLED STUDY; ELECTROENCEPHALOGRAM; FUNCTIONAL MAGNETIC RESONANCE IMAGING; FUNCTIONAL NEUROIMAGING; IMAGE QUALITY; INTERMETHOD COMPARISON; KERNEL METHOD; NEUROANATOMY; PRIORITY JOURNAL; PROCESS DEVELOPMENT; SPATIOTEMPORAL ANALYSIS; STATISTICAL MODEL; STATISTICAL PARAMETERS; TOPOGRAPHIC LATENT SOURCES; ADOLESCENT; ADULT; BIOLOGICAL MODEL; BRAIN; BRAIN MAPPING; ELECTROENCEPHALOGRAPHY; EVENT RELATED POTENTIAL; HUMAN; PHYSIOLOGY; TIME; YOUNG ADULT;

ADOLESCENT; ADULT; ALGORITHMS; BRAIN; BRAIN MAPPING; ELECTROENCEPHALOGRAPHY; EVENT-RELATED POTENTIALS, P300; HUMANS; MODELS, NEUROLOGICAL; MODELS, STATISTICAL; TIME FACTORS; YOUNG ADULT;

EID: 84904639201     PISSN: 10538119     EISSN: 10959572     Source Type: Journal    
DOI: 10.1016/j.neuroimage.2014.04.055     Document Type: Article

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