Consecutive Independence and Correlation Transform for Multimodal Fusion: Application to Eeg and Fmri Data
Mohammad A. B. S. Akhonda, Yuri Levin-Schwartz, Suchita Bhinge, Vince D. Calhoun, Tülay Adali
Abstract
Methods based on independent component analysis (ICA) and canonical correlation analysis (CCA) as well as their various extensions have become popular for the fusion of multimodal data as they minimize assumptions about the relationships among multiple datasets. Two important extensions that are widely used, joint ICA (jICA) and parallel ICA (pICA), make a number of simplifying assumptions that might limit their usefulness such as identical mixing matrices for jICA, and the requirement for the same number of components for jICA and pICA. In this paper, we propose a new, flexible hybrid method for fusion based on ICA and CCA, called consecutive independence and correlation transform (C-ICT), which relaxes the main limitations of jICA and pICA. We demonstrate performance advantages of C-ICT both through simulations and application to real medical data collected from schizophrenia patients and healthy controls performing an auditory oddball task (AOD).
BibTeX
@inproceedings{icassp2018_consecutiveindep,
title = {Consecutive Independence and Correlation Transform for Multimodal Fusion: Application to Eeg and Fmri Data},
author = {Mohammad A. B. S. Akhonda and Yuri Levin-Schwartz and Suchita Bhinge and Vince D. Calhoun and Tülay Adali},
booktitle = {ICASSP 2018},
year = {2018}
}