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

More documents

Recommendations

Info

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
2011, Neuroimage, for an unfair comparison using CORRMAP) across subjects and indeed, that is the case. What exactly the value of reporting this would be we do not know. CORRMAP operates on a map correlation approach and as it is shown in Fig. #9 and Fig. #15a, the ICs related to CI artifact are not consistently similar across subjects, thus CORRMAP will perform poorly. In the case of ADJUST (Mognon et al 2010), this tool was developed with the aim of clustering ICs representing eye blinks, lateral and vertical eye movements and discontinuities. Our prediction was that if ADJUST would be able to identify CI artifact related ICs, these ICs would be labeled as “discontinuities”. We ran ADJUST for the ESS. Since we performed our artifact correction in two steps: CORRMAP (used to find blinks, lateral eye movements and heartbeat artifacts) + CIAC (used to find CI artifact related ICs), we believe it is reasonable to compare the selection from CIAC with the sub-selection from ADJUST for “discontinuities”. The results are summarized in the table below, where we show the total number of ICs found by each method, as well as the number of ICs found by both methods (“common in both”). As expected the performance of ADJUST on finding ocular artifacts was good but on CI artifact related ICs was poor. When correcting all ICs found using ADJUST it was not possible to recover AEPs free from CI artifact, for any of the 18 CI users that comprised ESS. Nr. ICs identified CI CIAC ADJUST Common in user both 1 10 0 0 2 15 2 0 3 9 8 3 4 22 3 2 5 16 4 1 6 9 5 0 7 5 6 0 8 5 2 0 9 4 11 0 10 11 4 2 11 5 3 0 12 2 9 0 13 6 6 0 14 4 8 0 15 5 7 2 16 13 9 0 18 6 11 0 19 7 10 0 In the case of FASTER (Nolan et al, 2010), this tool was developed to receive as input raw data and run a sequence of steps that including different types of pre-processing and artifact correction. We could not run only one of the steps, therefore it was not possible to test the 13
Page 1 and 2: Elsevier Editorial System(tm) for H
Page 3 and 4: *Response to Reviewers Dear Dr. Can
Page 5 and 6: 8) Lines 137-138. Include a figure
Page 7 and 8: confident that the same thresholds
Page 9 and 10: eason to require a window of intere
Page 11 and 12: location information pointing consi
Page 13: As requested we show in the next fi
Page 17 and 18: *Highlights Highlights New algorit
Page 19 and 20: 27 28 29 30 31 32 33 34 35 36 37 38
Page 21 and 22: 81 82 83 84 85 86 87 88 89 90 91 92
Page 23 and 24: 136 137 138 139 140 141 142 143 144
Page 25 and 26: 189 190 191 192 193 194 195 196 197
Page 27 and 28: 242 243 244 245 246 247 248 249 250
Page 29 and 30: 294 295 296 297 298 299 300 301 302
Page 31 and 32: 348 349 350 351 352 353 354 355 356
Page 33 and 34: 402 403 404 405 406 407 408 409 410
Page 35 and 36: 458 459 460 461 462 463 464 465 466
Page 37 and 38: 511 512 513 514 515 516 517 518 519
Page 39 and 40: Figure(s) Click here to download hi
Page 41 and 42: Figure(s) Click here to download hi
Page 43: Table(s) Table 1. Cochlear implant

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

Create successful ePaper yourself

Delete template?

Save as template?