6533b837fe1ef96bd12a2019

RESEARCH PRODUCT

Combining PCA and multiset CCA for dimension reduction when group ICA is applied to decompose naturalistic fMRI data

Petri ToiviainenValeri TsatsishviliFengyu CongTapani Ristaniemi

subject

ta113MultisetPCAGroup (mathematics)business.industrydimension reductionSpeech recognitionDimensionality reductionPattern recognitionMusic listeningta3112naturalistic fMRIGroup independent component analysisPrincipal component analysistemporal cocatenationArtificial intelligenceCanonical correlationbusinessmultiset CCAMathematics

description

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. peerReviewed

yearjournalcountryeditionlanguage
2015-07-01
10.1109/ijcnn.2015.7280722https://doi.org/10.1109/IJCNN.2015.7280722