Consecutive Independence and Correlation Transform for Multimodal Fusion: Application to Eeg and Fmri Data

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 wid...

Full description

Saved in:
Bibliographic Details
Main Authors: Akhonda, Mohammad A. B. S., Levin-Schwartz, Yuri, Bhinge, Suchita, Calhoun, Vince D., Adali, Tulay
Format: Conference Proceeding
Language:English
Subjects:
Brain modeling
Canonical Correlation Analysis
Correlation
Data Fusion
EEG
Electroencephalography
Feature extraction
FMRI
Functional magnetic resonance imaging
Independent Component Analysis
Task analysis
Transforms
Citations: Items that cite this one
Online Access:Request full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary: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).
ISSN:2379-190X
DOI:10.1109/ICASSP.2018.8462031