| Literature DB >> 20824156 |
Tzyy-Ping Jung1, Scott Makeig, Martin J McKeown, Anthony J Bell, Te-Won Lee, Terrence J Sejnowski.
Abstract
The analysis of electroencephalographic (EEG) and magnetoencephalographic (MEG) 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 (fMRI) 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.Entities:
Year: 2001 PMID: 20824156 PMCID: PMC2932458 DOI: 10.1109/5.939827
Source DB: PubMed Journal: Proc IEEE Inst Electr Electron Eng ISSN: 0018-9219 Impact factor: 10.961