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Indexed by:会议论文

Date of Publication:2013-05-26

Included Journals:EI、CPCI-S、Scopus

Page Number:1310-1314

Key Words:independent component analysis; semi-blind; acoustic features; natural music; functional magnetic resonance imaging

Abstract:This study presents a method to analyze blood-oxygen-level-dependent (BOLD) functional magnetic resonance imaging (tMRI) signals associated with listening to continuous music. Semi-blind independent component analysis (ICA) was applied to decompose the tMRI data to source level activation maps and their respective temporal courses. The unmixing matrix in the source separation process of ICA was constrained by a variety of acoustic features derived from the piece of music used as the stimulus in the experiment. This allowed more stable estimation and extraction of more activation maps of interest compared to conventional ICA methods.