Performance comparison of machine learning algorithms and number of independent components used in fMRI decoding of belief vs. disbelief

NeuroImage

Volumn 56, Issue 2, 2011, Pages 544-553

  Douglas, P K ^a   Harris, Sam ^a   Yuille, Alan ^a   Cohen, Mark S ^a  

^a UNIVERSITY OF CALIFORNIA   (United States)

Author keywords

Classifier; FMRI; Independent component analysis; Interpretability; Machine learning; Optimization

Indexed keywords

ACCURACY; ADULT; ARTICLE; AUTOANALYSIS; BRAIN MAPPING; BRAIN REGION; CLASSIFICATION ALGORITHM; CONTRAST RADIOGRAPHY; CONTROLLED STUDY; DATA ANALYSIS; DATA EXTRACTION; DECISION TREE; FEMALE; FUNCTIONAL MAGNETIC RESONANCE IMAGING; HUMAN; HUMAN EXPERIMENT; INDEPENDENT COMPONENT ANALYSIS; INTERMETHOD COMPARISON; LEARNING ALGORITHM; NEUROIMAGING; NORMAL HUMAN; PERFORMANCE MEASUREMENT SYSTEM; PRIORITY JOURNAL; PROCESS OPTIMIZATION; SUPPORT VECTOR MACHINE; TASK PERFORMANCE;

ADOLESCENT; ADULT; ALGORITHMS; ARTIFICIAL INTELLIGENCE; BRAIN; BRAIN MAPPING; FEMALE; HUMANS; IMAGE INTERPRETATION, COMPUTER-ASSISTED; MAGNETIC RESONANCE IMAGING; MALE; MIDDLE AGED; PATTERN RECOGNITION, AUTOMATED; YOUNG ADULT;

EID: 79955007464     PISSN: 10538119     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.neuroimage.2010.11.002     Document Type: Article

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