icaOcularCorrection: Independent Components Analysis Based Artifact Correction

Tremblay, Antoine

icaOcularCorrection: Independent Components Analysis Based Artifact Correction

dc.contributor.author	Tremblay, Antoine
dc.date.accessioned	2015-09-22T11:47:01Z
dc.date.available	2015-09-22T11:47:01Z
dc.date.issued	2015
dc.description	Removes eye-movement and other types of known (i.e., recorded) or unknown (i.e., not recorded) artifacts using the fastICA package. The correction method proposed in this package is largely based on the method described in on Flexer, Bauer, Pripfl, and Dorffner (2005). The process of correcting electro- and magneto-encephalographic data (EEG/MEG) begins by running function ``icac'', which first performs independent components analysis (ICA) to decompose the data frame into independent components (ICs) using function ``fastICA'' from the package of the same name. It then calculates for each trial the correlation between each IC and each one of the noise signals -- there can be one or more, e.g., vertical and horizontal electro-oculograms (VEOG and HEOG), electro-myograms (EMG), electro-cardiograms (ECG), galvanic skin responses (GSR), and other noise signals. Subsequently, portions of an IC corresponding to trials at which the correlation between it and a noise signal was at or above threshold (set to 0.4 by default; Flexer et al., 2005, p. 1001) are zeroed-out in the source matrix, ``S''. The user can then identify which ICs correlate with the noise signals the most by looking at the summary of the ``icac'' object (using functions ``summary.icac'' and ``plot_smry''), the scalp topography of the ICs (using function ``topo_ic''), the time courses of the ICs (using functions ``plot_tric'' and ``plot_nic''), and other diagnostic plots such as ``topo_ic''. Once these ICs have been identified, they can be completely zeroed-out using function ``update.icac'' and the resulting correction checked using functions ``plot_avgba'' and ``plot_trba''. Some worked-out examples with R code are provided in the package vignette on DalSpace	en_US
dc.description.sponsorship	SSHRC doctoral and postdoctoral fellowships to Antoine Tremblay.
dc.identifier.uri	http://hdl.handle.net/10222/63070
dc.subject	EEG	en_US
dc.subject	electroencephalogram	en_US
dc.subject	ERP	en_US
dc.subject	event-related potentials	en_US
dc.subject	MEG	en_US
dc.subject	magnetoencephalogram	en_US
dc.subject	event-related fields	en_US
dc.subject	event-related magnetic fields	en_US
dc.subject	package	en_US
dc.subject	denoising	en_US
dc.subject	noise correction	en_US
dc.subject	blinks	en_US
dc.subject	eye-movement artifact correction	en_US
dc.title	icaOcularCorrection: Independent Components Analysis Based Artifact Correction	en_US
dc.type	Software	en_US