@article{MakeigEtAl1996,
	title = {{Independent component analysis of electroencephalographic data}},
	author = {S. Makeig and A.J. Bell and T.P. Jung and T.J. Sejnowski},
	journal = {Advances in Neural Information Processing Systems},
	pages = {145--151},
	publisher = {The MIT Press},
	volume = {8},
	year = {1996},
	biburl = {http://www.bibsonomy.org/bibtex/2f2350ac30fabf4683e73d6bdca4de11f/tmalsburg},
	abstract = {Because of the distance between the skull and brain and their different resistivities, electroencephalographic (EEG) data collected from
any point on the human scalp includes activity generated within
a large brain area. This spatial smearing of EEG data by volume
conduction does not involve signi cant time delays, however, suggesting that the Independent Component Analysis (ICA) algorithm
of Bell and Sejnowski 1] is suitable for performing blind source sep-
aration on EEG data. The ICA algorithm separates the problem of
source identi cation from that of source localization. First results
of applying the ICA algorithm to EEG and event-related potential
(ERP) data collected during a sustained auditory detection task
show: (1) ICA training is insensitive to di erent random seeds. (2)
ICA may be used to segregate obvious artifactual EEG components
(line and muscle noise, eye movements) from other sources. (3) ICA
is capable of isolating overlapping EEG phenomena, including alpha and theta bursts and spatially-separable ERP components, to
separate ICA channels. (4) Nonstationarities in EEG and behav-
ioral state can be tracked using ICA via changes in the amount of
residual correlation between ICA- ltered output channels.
},
	keywords = {componentanalysis dataanalysis eeg electrophysiology erp modeling neurophysiology sourceanalysis }
}