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<span style="font-variant-ligatures: normal; font-variant-east-asian: normal; font-variant-position: normal; vertical-align: baseline; white-space: pre-wrap;" class="">Dear experts,</span></div>
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<span style="white-space: pre-wrap; font-variant-ligatures: normal; font-variant-east-asian: normal; font-variant-position: normal; vertical-align: baseline;" class="">We recently submitted a paper where we use a cluster permutation analysis of ERFs (testing
conditions <span style="color: rgb(34, 34, 34); font-family: sans-serif; font-variant-ligatures: normal; orphans: 2; white-space: normal; widows: 2;" class="">
exchangeability)</span>. The EEG was sampled at 500 Hz and low-pass filtered at 40 Hz during preprocessing. One of the reviewers has indicated that u</span><span style="white-space: pre-wrap;" class="">sing this temporal resolution for the analysis seems unnecessary
given the low-pass filter, which makes each time point not independent due to smoothing. He further asks: "</span><span style="white-space: pre-wrap;" class="">Do all key significant components replicate if the analysis is performed using a mean amplitude
that is averaged for larger time bins (25 ms bins) instead of individual time points?"</span></div>
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I understand that when doing parametric analysis one might want to reduce the number of comparisons by averaging ERF samples over time bins. But here we solve the MCP with the cluster analysis. But it also seems like the reviewer is concerned about non-independecy
in the data resulting from low-pass filtering. I wanted to check with you: can smoothing time-series indeed compromise the cluster-based analysis (e.g. affect the cluster size)?</div>
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Thank you, </div>
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Kind regards, </div>
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Irina</div>
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