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Enhanced detection of artifacts in EEG data using higher-order statistics and independent component analysis
2007
NeuroImage
Detecting artifacts produced in EEG data by muscle activity, eye blinks and electrical noise is a common and important problem in EEG research. It is now widely accepted that independent component analysis (ICA) may be a useful tool for isolating artifacts and/or cortical processes from electroencephalographic (EEG) data. We present results of simulations demonstrating that ICA decomposition, here tested using three popular ICA algorithms Infomax, SOBI, and FastICA, can allow more sensitive
doi:10.1016/j.neuroimage.2006.11.004
pmid:17188898
pmcid:PMC2895624
fatcat:h33zapbot5cxrlbpnxoqniqj5q