Journal
NEUROIMAGE
Volume 28, Issue 2, Pages 326-341Publisher
ACADEMIC PRESS INC ELSEVIER SCIENCE
DOI: 10.1016/j.neuroimage.2005.05.032
Keywords
electroencephalography (EEG); linear integration; source estimation; generalized eigenvalue decomposition; brain computer interfaces
Funding
- NIBIB NIH HHS [EB004730] Funding Source: Medline
Ask authors/readers for more resources
In this paper, we describe a simple set of recipes for the analysis of high spatial density EEG. We focus on a linear integration of multiple channels for extracting individual components without making any spatial or anatomical modeling assumptions, instead requiring particular statistical properties such as maximum difference, maximum power, or statistical independence. We demonstrate how corresponding algorithms, for example, linear discriminant analysis, principal component analysis and independent component analysis, can be used to remove eye-motion artifacts, extract strong evoked responses, and decompose temporally overlapping components. The general approach is shown to be consistent with the underlying physics of EEG, which specifies a linear mixing model of the underlying neural and non-neural current sources. (c) 2005 Elsevier Inc. All rights reserved.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available