Elsevier Editorial System(tm) for Hearing Research Manuscript Draft ...

eason to require a window of interest as an input parameter is to prevent the selection of ICs
that could result from a poor un-mixing. These ICs may represent brain activity of interest
contaminated by residual CI artifact. As our goal is the attenuation of the CI artifact without
affecting the AEPs, we wanted to make sure that these ICs are not selected. The only way to
overcome this overlapping problem is by developing better ICA algorithms that would
improve the un-mixing from CI artifact and brain sources.
18) Pages 6-7, Lines 161-162, "Details about the clinical profile of the CI users have been
described elsewhere." Even so, it would be helpful to have some more details included in the
current manuscript, especially details related to the implant type, stimulation strategy, etc.
Because this is mostly a paper describing technical selection of CI related artifacts, at least
CI stimulation details should be included, so that the reader has easier access to the
information. A brief table would likely suffice for such information.
Reply: A table with the requested information has been included into the revised manuscript.
19) Page 7, Line 180, & Line 182: ".filtered offline from 1-40 Hz"; ".down-sampled to 500
Hz". How does filtering and/or down-sampling the data affect the ICA routine? And, how
does affect the topography and shape of the artifact ICs? Do different parameters here give
different results? Would a researcher interested in 80 Hz activity be more (or, less)
susceptible to mis-characterized artifacts?
Reply: It is known that high-pass filtering has beneficial effects on the ICA decomposition, as
ICA assumes stationarity. We commonly use a 1 Hz high-pass filter in order to remove drifts
from the data and improve the ICA decomposition (for instance, see Debener, S. et al, (2010).
Using ICA for the Analysis of Multi-Channel EEG Data. In M. Ullsperger & S. Debener
(Eds.), Simultaneous EEG and fMRI (pp. 121-135). New York: Oxford University Press).
Regarding the low-pass filter we choose a cut-off that is used traditionally in ERP research,
since normally the responses of interest are below this frequency range. However the filter
settings seem to not directly influence the maximum number of CI artifact-related ICs found
in the data, as well as their characteristics. For instance, in a study from Debener et al (2008,
Psychophysiology) the filter settings used were from 1-80 Hz, ICs related to the CI artifact
could be identified and the AEPs could be reconstructed.
20) Page 7, Line 184: "Extended infomax ICA." Would this procedure be limited to only this
ICA routine? One concern here, especially if one is clinically minded, is that in many cases
ICA can be both computationally and time intensive. Would other ICA routines (e.g.,
FastICA, JADER, etc.) be less useful? More useful? In other words, would these estimations
be limited by the type of ICA routine that is chosen?
Reply: It is true that some ICA routines are both computationally and time intensive. We use
extended-infomax because it is our experience that it provides reliable ICA decompositions
for high-density EEG recordings, independently of the presence of the CI artifact. Although
there is a lack of validation studies comparing ICA algorithms, guidance can be found in the
EEGLAB website
(cf. http://sccn.ucsd.edu/wiki/Chapter_09:_Decomposing_Data_Using_ICA). According to
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