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VGU RESEARCH REPOSITORY


Please use this identifier to cite or link to this item: https://epub.vgu.edu.vn/handle/dlibvgu/206
Title: Comparison analysis of ICA versus MCA-KSVD blind source separation on task-related FMRI data
Authors: Le Hoang Nam 
Nguyen Ngoc Khang 
Nguyen Minh Hien 
Keywords: Algorithm design;Visualization
Issue Date: 2017
Source: http://apsipa2017.org/
Conference name: 9th Asia-Pacific Signal and Information Processing Association Annual Summit and Conference 
Abstract: 
Decomposition of working brain into meaningful clusters of regions is an important step to understand brain functionality. Blind source separation algorithms can achieve this, which results in diverse outcomes dependent upon underlying assumptions of the decomposition algorithm in use. The conventional data-driven method to detect brain functional networks is the Independent Component Analysis (ICA). The ICA method assumes decomposed components are statistically independent of each other. However, such a mathematical assumption is physiologically uncertain in regard to its applications to functional MRI (fMRI) studies. A recently proposed MCA-KSVD method, which stands for Morphological Component Analysis implemented using a K-SVD algorithm, relaxes the independence assumption imposed by the ICA method. In this study, a comprehensive comparison between the conventional ICA and MCA-KSVD methods was conducted in the presence of various simulated noise conditions. Experimental results showed that in a task-related fMRI experiment, the MCA-KSVD method successfully identified same networks as those detected by the ICA method and had advantages of better signal localization and spatial resolution. However, improper choices of the sparsity parameter and the number of trained atoms introduced phenomena, namely signal leakage, signal splitting and signal ambiguity. The MCA-KSVD method could be used as an alternative or in parallel with the ICA method, but with careful consideration of model parameter selection.
Duration: 12-15/12/2017
Conference venue: Kuala Lumpur, Malaysia
URI(1): http://epub.vgu.edu.vn/handle/dlibvgu/206
Appears in Collections:Conference papers

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