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

Behavioral and Brain Functions, 7; 30; for a very recent example). Here we have adopted
exactly the same strategy and believe that this is in fact a valid approach. We are confident
that aiming towards objective methods to identify ICs representing the CI artifact is necessary
and constitutes a clear improvement over manual component selection.
Furthermore the reviewer also requests a comparison to other tools that classify ICs. Please
see our detailed response below (issue #28). CIAC was developed to identify ICs objectively
across subjects and although it requires the evaluation of spatial and temporal properties of
the ICs, we do not consider it to be “very elaborated”, as it is based on only two features and
the definition of only three parameters. Other studies explored a much larger feature space,
e.g. Winkler et al., 2011 (starting with 38 features) or Nolan et al., 2010 (FASTER includes
six main steps and for each step more than five parameters can be adjusted). Accordingly,
CIAC is a rather simple, robust yet flexible tool.
27) The steps of CIAC seem ad hoc and not grounded in any understanding of the biophysics
or features of the CI electrical artifact, and as such does not offer any new contribution over
and above present attempts for 'automatic' selection.
Reply: As discussed above, it is indeed the case that the electrical artifact is not well
understood at present. Here the problem is again that the ground truth is not known, since
pure artifact only recordings cannot be made. At least we are not aware of any procedure
measuring the artifact exclusively, using an adequate forward model. We would greatly
appreciate if the reviewer could point us to any reference describing the electrical CI artifact
in detail. Accordingly it is not well understood which electrical properties of the CI device are
represented in the EEG recording. The steps implemented in CIAC are grounded in the
properties of the ICs representing the CI artifact, since it is an ICA-based tool. We believe
that CIAC is a significant contribution towards a more objective, reliable and efficient
recovery of AEPs from CI users. Other available tools were tested and the results obtained
were not satisfactory (please see #28 below).
28) While the focus in automatic artifact/IC detection has been on eye movement and muscle
artifacts, the *procedures* of automatic identification of (un)desired features should be
generic, and should be possible to tune for any required features, and as such comparison
with existing tools is required. So a comparison with Ting et al, Morgan et al or even the
authors own previous work Viola et al, 2009 should be considered. Alternatively, a clear
justification of a new method based, for example, on the uniqueness of the properties or
biophysics of electric artifact would be needed (over an above just stating " electrical CI
artifacts have a particular signature in the spatial and temporal domain as illustrated in
Figure 1"). The setting of the thresholds seems arbitrary and their selection may, in some
cases, require tuning, which questions the claim of automaticity, which perhaps should be
revised.
Reply: We had run comparisons between our method and other available tools but had
decided to not include them in the manuscript. The reason for that is that we do not find it fair
reporting poor performance of a tool when a priori information makes it very likely that it
cannot work. For instance, as explained previously (see #9), CORRMAP is not expected to
find ICs representing the CI artifact (or ICs representing neural activity; see Wessel et al.,
12