Imaging brain dynamics using independent component analysis

Author

Jung, Tzyy-Ping ; Makeig, Scott ; McKeown, Martin J. ; Bell, Anthony J. ; Lee, Te-Won ; Sejnowski, Terrence J.

Author_Institution

California Univ., San Diego, La Jolla, CA, USA

Volume

89

Issue

7

fYear

2001

fDate

7/1/2001 12:00:00 AM

Firstpage

1107

Lastpage

1122

Abstract

The analysis of electroencephalographic and magnetoencephalographic recordings is important both for basic brain research and for medical diagnosis and treatment. Independent component analysis (ICA) is an effective method for removing artifacts and separating sources of the brain signals from these recordings. A similar approach is proving useful for analyzing functional magnetic resonance brain imaging data. In this paper, we outline the assumptions underlying ICA and demonstrate its application to a variety of electrical and hemodynamic recordings from the human brain

Keywords

Jacobian matrices; biomedical MRI; covariance matrices; decorrelation; electroencephalography; gradient methods; magnetoencephalography; medical image processing; reviews; time series; EEG; Jacobian matrix; MEG; alpha ringing; artifacts removal; blind source separation; brain dynamics imaging; correlations removal; covariance matrix; functional MRI; gradient descent algorithm; hemodynamic recordings; independent component analysis; time series; Brain; Image analysis; Independent component analysis; Magnetic analysis; Magnetic recording; Magnetic resonance; Magnetic resonance imaging; Magnetic separation; Medical diagnosis; Medical treatment;

fLanguage

English

Journal_Title

Proceedings of the IEEE

Publisher

ieee

ISSN

0018-9219

Type

jour

DOI

10.1109/5.939827

Filename

939827