Elsevier Editorial System(tm) for Hearing Research Manuscript Draft ...
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high sensitivity and a good specificity when compared to the results of classification by two experts. It<br />
is worth noting that the ability of semi-automatically identifying CI artifact related ICs relies mainly<br />
on the general quality of the ICA decomposition, which depends on EEG preprocessing and other<br />
aspects not covered here (Debener, et al., 2010). The existence of ICs where the artifact is not well<br />
disentangled from brain activity (or other types of artifact) may present some challenges. It is not<br />
known how many sensors may be needed to accurately identify CI artifacts and it is also not well<br />
understood which components of the device contribute more to the artifact. There<strong>for</strong>e it would be<br />
beneficial to validate CIAC with other EEG montages and types of stimuli.<br />
It is important to highlight that CIAC benefits from experimental designs where the duration of the<br />
stimuli does not overlap with the responses of interest. This was not the case in the TNS data where<br />
the P2 responses were difficult to reconstruct <strong>for</strong> some CI users. Accordingly an experimental design<br />
where duration of the auditory stimuli is longer than the cortical response interval of interest enlarges<br />
the probability to reconstruct good quality AEPs. The use of short stimuli may prevent this type of<br />
issues. However this also limits the type of studies that can be implemented. It is expected that some<br />
of these current limitations may be overcome with the implementation of new ICA algorithms that<br />
may allow a better separation between artifacts and other sources. When it comes to the comparison<br />
between CIAC and experts, the results should be considered preliminary, since only two experts<br />
participated in the validation procedure. Given the large number of decisions necessary (approximately<br />
number of electrodes x number of individuals) it is likely that users show some degree of<br />
inconsistency, thus limiting the reliability of the resulting AEPs. Different experts may also apply<br />
different criteria. For instance, it is our experience that experts may ignore noise related ICs<br />
contaminated with residual CI artifact since these normally explain a small amount of variance in the<br />
AEPs. Moreover experts could be biased by their past experience, if they, <strong>for</strong> instance, only had<br />
experience with datasets collected from CI users using devices from a specific manufacturer or<br />
collected with a particular electrode montage. It is also worth noting that the number of researchers<br />
experienced with the selection of CI artifact related ICs is likely small, which hinders the wider use of<br />
AEPs <strong>for</strong> the assessment of auditory rehabilitation. Accordingly a comparison of CIAC with more<br />
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