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

16) Of minor concern (and slightly related to the above question of robustness), is the clinical
applicability of this tool? Realizing, of course, that EEGLAB is a research tool, and that ICA
assumes at least as many sensors as underlying sources (very rare in a clinical setting), how
might this be extended for use other than in an experimental / laboratory setting? Throughout
the paper, especially nearing the end of the discussion, clinical application is frequently
mentioned. The authors may consider great care in generalizing such a tool at the clinical
level, until some of the robustness issues are addressed.
Reply: We agree that in clinical settings the measurement of AEPs takes place in conditions
that differ from the multi-channel EEG recordings used to validate CIAC. However we
believe that moving towards a more objective method to attenuate CI artifacts can contribute
to an increase in the number and in the quality of AEP studies in research settings. It is
expected that outputs from these studies would contribute to developments also at the clinical
level. Moreover it is also expected that the use of a tool such as CIAC will contribute to a
better understanding of the characteristics of the CI artifact and in the long run could even be
possible to develop a truly automatic method. In addition, we would like to point out that
high-density EEG recordings are already possible in clinical settings. The geodesic sensor net
(offered by Electrical Geodesics, www.egi.com) for instance allows a very short cap
preparation time (few minutes for up to 256-channels!), and the system we currently favor
allows, with some experience, for a quite reasonable cap application time (approx. 15 minutes
for 64-channnel recordings). Even if further research (see our response to issue #15b)
revealed that high-density recordings are necessary to remove the artifact proper we would
not think that this prevents clinical application.
17) Page 5, Line 123: ".3) a time window of interest for the AEP response". It seems that
this largely constricts the robustness of the procedure. One important question related to
AEPs in CI users is the variable latency of the AEP response. If the user attempts to
compensate for unknown latencies by, for example, using a large window of interest, how
does this change the effectiveness of IC identification? What about the case of the
experimentalist that might be using a data mining approach with unknown prior information
about the responses or response differences due to experimental conditions? Can this
procedure still be of use to such a user? For example, what happens if the entire poststimulus
window is used as the window of interest? Are there other approaches to the IC
estimation that can help to overcome such a limitation?
Reply: We agree that the individual differences in the latency of the AEP response in CI users
can be large. However we expect users to be interested in broader ranges of auditory activity
that can last (or peak at) 100-200 ms or even longer, as for instance when exploring the N1-P2
complex or the MMN. Thus we recommend that the users select a window of interest larger
than 100 ms in order to account for any possible jitter in the responses. A conflict can only
occur if the edge of the time window of interest falls in the time window representing the
offset of the CI artifact. In these cases, the window of interest should be shortened, with the
risk of losing information about later responses, as it proved to be the case for the TNS where
it was not possible to analyze the P2 response. In the case of language studies where the focus
is on AEP components such as N400 or P600, it is recommend that the stimuli used are longer
than 600 ms. The minimum duration of the stimuli will depend if the user also wants to
investigate early responses such as the N1-P2 complex (please see below #23)). The main
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