Combining PCA and multiset CCA for dimension reduction when group ICA is applied to decompose naturalistic fMRI data
Tsatsishvili, V., Cong, F., Toiviainen, P., & Ristaniemi, T. (2015). Combining PCA and multiset CCA for dimension reduction when group ICA is applied to decompose naturalistic fMRI data. In IJCNN 2015 : International Conference on Neural Networks (pp. 1-6). Institute of Electrical and Electronic Engineers. doi:10.1109/IJCNN.2015.7280722
© 2015 IEEE. This is an authors' post-print version of an article whose final and definitive form has been published in the conference proceeding by IEEE.
An extension of group independent component analysis (GICA) is introduced, where multi-set canonical correlation analysis (MCCA) is combined with principal component analysis (PCA) for three-stage dimension reduction. The method is applied on naturalistic functional MRI (fMRI) images acquired during task-free continuous music listening experiment, and the results are compared with the outcome of the conventional GICA. The extended GICA resulted slightly faster ICA convergence and, more interestingly, extracted more stimulus-related components than its conventional counterpart. Therefore, we think the extension is beneficial enhancement for GICA, especially when applied to challenging fMRI data.