SACICA: A sparse approximation coefficient-based ICA model for functional magnetic resonance imaging data analysis

Journal of Neuroscience Methods

Volumn 216, Issue 1, 2013, Pages 49-61

  Wang, Nizhuan ^a   Zeng, Weiming ^a   Chen, Lei ^a  

^a SHANGHAI MARITIME UNIVERSITY   (China)

Author keywords

FMRI; ICA; Lp norm; Sparsity; Wavelet packet decomposition

Indexed keywords

ARTICLE; DATA ANALYSIS; FUNCTIONAL MAGNETIC RESONANCE IMAGING; INDEPENDENT COMPONENT ANALYSIS; PRIORITY JOURNAL; SENSITIVITY ANALYSIS; SPARSE APPROXIMATION COEFFICIENT BASED INDEPENDENT COMPONENT ANALYSIS; WAVELET ANALYSIS;

ALGORITHMS; ARTIFICIAL INTELLIGENCE; BRAIN; BRAIN MAPPING; DATA INTERPRETATION, STATISTICAL; HUMANS; IMAGE INTERPRETATION, COMPUTER-ASSISTED; MAGNETIC RESONANCE IMAGING; NERVE NET; PATTERN RECOGNITION, AUTOMATED; PRINCIPAL COMPONENT ANALYSIS; REPRODUCIBILITY OF RESULTS; SENSITIVITY AND SPECIFICITY;

EID: 84877894720     PISSN: 01650270     EISSN: 1872678X     Source Type: Journal    
DOI: 10.1016/j.jneumeth.2013.03.014     Document Type: Article

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