WASICA: An effective wavelet-shrinkage based ICA model for brain fMRI data analysis

• Published in: Journal of neuroscience methods Vol. 246; pp. 75 - 96
• Main Authors: Wang, Nizhuan, Zeng, Weiming, Shi, Yingchao, Ren, Tianlong, Jing, Yanshan, Yin, Jun, Yang, Jiajun
• Format: Journal Article
• Language: English
• Published: Netherlands Elsevier B.V 15.05.2015
• Subjects: Algorithms; Brain - blood supply; Brain Mapping; Computer Simulation; fMRI; Humans; ICA; Image Processing, Computer-Assisted; Magnetic Resonance Imaging; Models, Theoretical; Normal Distribution; Oxygen - blood; Principal Component Analysis; Reproducibility of Results; Sparse approximation; Wavelet packet decomposition; Wavelet shrinkage; fMRI; Wavelet shrinkage; Sparse approximation; Wavelet packet decomposition; ICA

•Wavelet shrinkage leads to sparse approximation of the sources and suppresses noise.•Sparse approximation coefficients set satisfies the super-Gaussian distribution.•WP reconstruction generates an effective approximation with nuisance exclusion.•WASICA exhibits the superiority in signal recovery an...