Package: icaOcularCorrection
Type: Package
Title: Independent Components Analysis (ICA) based artifact correction.
Version: 2.1
Date: 2013-06-12
Depends: fastICA, mgcv
Author: Antoine Tremblay, NeuroCognitive Imaging Lab, Dalhousie
University
Maintainer: Antoine Tremblay <trea26@gmail.com>
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 either zeroed-out in
the source matrix, ``S'', or subtracted from the data that was
passed to function ``icac''. The user can then identify which
ICs correlate with the noise signals the most by looking at the
summary of the ``icac'' object (using function
``summary.icac''), 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. Once these ICs have been identified, they can
be completely zeroed-out or subtracted 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 following sections.
Please contact the package maintainer to obtain the data to run
the examples.
Suggests: wavethresh
BuildVignettes: no
License: GPL-2
LazyLoad: yes
Packaged: 2013-06-12 18:07:21 UTC; antoine
NeedsCompilation: no
Repository: CRAN
Date/Publication: 2013-06-12 20:18:57