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
