Parallel independent component analysis for multimodal analysis: Application to FMRI and EEG data

Liu Jingyu, Vince Calhoun

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

This paper presents the technique of parallel independent component analysis (paraICA) with adaptive dynamic constraints applied to two datasets simultaneously. As a framework to investigate the integration of data from two imaging modalities, this method is dedicated to identify components of both modalities and connections between them through enhancing intrinsic interrelationships. The performance is assessed by simulations under different conditions of signal to noise ratio, connection strength and estimation of component order. An application to functional magnetic resonance images and electroencephalography data is conducted to illustrate the usage of paraICA. Results show that paraICA provides stable results and can identify the linked components with a relatively high accuracy. The application exhibits the ability to discover the connection between brain maps and event related potential time courses, and suggests a new way to investigate the coupling between hemodynamics and neural activity.

Original languageEnglish (US)
Title of host publication2007 4th IEEE International Symposium on Biomedical Imaging
Subtitle of host publicationFrom Nano to Macro - Proceedings
Pages1028-1031
Number of pages4
DOIs
StatePublished - Nov 27 2007
Externally publishedYes
Event2007 4th IEEE International Symposium on Biomedical Imaging: From Nano to Macro; ISBI'07 - Arlington, VA, United States
Duration: Apr 12 2007Apr 15 2007

Publication series

Name2007 4th IEEE International Symposium on Biomedical Imaging: From Nano to Macro - Proceedings

Other

Other2007 4th IEEE International Symposium on Biomedical Imaging: From Nano to Macro; ISBI'07
CountryUnited States
CityArlington, VA
Period4/12/074/15/07

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Keywords

  • Electroencephalography
  • Functional magnetic resonance imaging
  • Independent component analysis
  • Parallel process

ASJC Scopus subject areas

  • Electrical and Electronic Engineering
  • Medicine(all)

Cite this

Jingyu, L., & Calhoun, V. (2007). Parallel independent component analysis for multimodal analysis: Application to FMRI and EEG data. In 2007 4th IEEE International Symposium on Biomedical Imaging: From Nano to Macro - Proceedings (pp. 1028-1031). [4193464] (2007 4th IEEE International Symposium on Biomedical Imaging: From Nano to Macro - Proceedings). https://doi.org/10.1109/ISBI.2007.357030