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

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top row. Contrary to several conventional types of EEG artifacts, which are represented by ICs with
similar topographies across subjects (Viola, et al., 2009), the CI artifact topographies can be
substantially different across individuals. On the other hand, for the same CI user, the topographies of
the ICs reflecting the artifact may share a substantial degree of similarity (not shown). We could not
find any relationship between the topographical pattern of the ICs and the type of CI device or other
related properties. However when inspecting the temporal properties of the CI related ICs from
different CI users it is evident that they share very similar profiles. The largest activity takes place
during the onset and/or offset of the artifact as can be seen both for the time-locked average of the IC
activation (IC ERP) and its first temporal derivative (Figure 1, middle and bottom rows). On the other
hand, ICs representing for instance late auditory cortex related activity usually have largest deflections
in the time window corresponding to the N1-P2 responses (100-250 ms) as illustrated in Figure 1.
Based on these observations we implemented an algorithm that combines spatial and temporal
information and selects CI related ICs using three steps and three thresholds. Figure 2 shows a
schematic flow chart of the cochlear implant artifact correction (CIAC) algorithm. As a starting point
three user inputs are required: 1) ICs from one or more EEG dataset, epoched to the same auditory
stimuli of interest; 2) the duration of the auditory stimuli; 3) a time window of interest for the AEP
response. A loop of three steps is then computed for the ICs from each single dataset. In the first step
CIAC selects the ICs with RV larger than a pre-defined threshold (RV > Thr_rv). This is an exclusion
step to avoid the further selection of ICs that would likely represent brain related activity. In the
second step the temporal derivative of the ERP is calculated for each of the ICs which are part of the
subset selected in the first step. Then the ratio is computed between the root mean square (RMS)
amplitude of the IC temporal derivative in the artifact onset/offset time window (derived from the
duration of the auditory stimuli – user input) and the RMS amplitude for the time window where the
responses of interest are expected (user input). The IC with the largest ratio is selected as a
topographical template for that particular CI user. This IC is the one reflecting the strongest artifact
profile, and its topography is then going to be correlated with all other topographies from the same CI
user. In the third step ICs are selected if at least one of these two criteria is met: having a ratio larger
than a pre-defined threshold (ratio > Thr_deriv) or the correlation between topography and the CI
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