Title :
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
fDate :
7/1/2001 12:00:00 AM
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;
Journal_Title :
Proceedings of the IEEE
