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Dear Matthias,
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<div class="">Don’t worry. The partial rejection method indeed cuts original trials in smaller portions, but it preserves the local time axes, relative to the original trials. </div>
<div class="">Since ft_freqanalysis returns per ‘segment’ TFRs with numeric data only for those portions of data for which the time windows are entirely filled with data (and nans otheriwse, with the average across segments is computed as a nanmean) there is
no worry about ‘edge artifacts’ as such. The only thing to keep in the back of your head is that different time-frequency points may end up having slightly different degrees of freedom in the average.</div>
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<div class="">Best wishes,</div>
<div class="">Jan-Mathijs</div>
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<div class="">On 14 May 2018, at 14:39, Matthias Franken <<a href="mailto:Matthias.Franken@UGent.be" class="">Matthias.Franken@UGent.be</a>> wrote:</div>
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<span lang="EN-US" class="">Dear all,</span><span lang="en-BE" class=""><o:p class=""></o:p></span></div>
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<span lang="EN-US" class=""> </span><span lang="en-BE" class=""><o:p class=""></o:p></span></div>
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<span lang="EN-US" class="">I have an EEG dataset that consists of quite long trials (>20s). Therefore, For artifact rejection I would like to only reject the part of the trial that contains the artifact, rather than throwing away the entire trial, and thus
I will use the cfg.artfctdef.method = ‘partial’; option in ft_rejectartifact.</span><span lang="en-BE" class=""><o:p class=""></o:p></span></div>
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<span lang="EN-US" class="">However, this leads to some trials being cut in two shorter “trials” (i.e., the part before the artifact and the part after the artifact). I’m guessing this will lead to edge artifacts in my time-frequency analysis, not only at the
edges of the original trials, but also at time points where artifacts were cut out. SO I’m not sure how to deal with this.</span><span lang="en-BE" class=""><o:p class=""></o:p></span></div>
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<span lang="EN-US" class=""> </span><span lang="en-BE" class=""><o:p class=""></o:p></span></div>
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<span lang="EN-US" class="">I guess one option would be to perform the time-frequency analysis first, and afterwards cut out the artifacts, although (1) that might lead to the artifact smearing into data points that were not cut out, depending on the time window
width during frequency analysis, and (2) the artifacts identified visually using ft_databrowser are define din ample numbers, and the result from ft_freqanalysis downsamples the data.</span><span lang="en-BE" class=""><o:p class=""></o:p></span></div>
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<span lang="EN-US" class=""> </span><span lang="en-BE" class=""><o:p class=""></o:p></span></div>
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<span lang="EN-US" class="">Did anyone face similar issues or have ideas on how to get around this?</span><span lang="en-BE" class=""><o:p class=""></o:p></span></div>
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<span lang="en-BE" class=""><o:p class=""> </o:p></span></div>
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<span lang="en-BE" class=""><o:p class=""> </o:p></span></div>
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<span lang="en-BE" class=""><br class="">
________________________________________<br class="">
Matthias Franken<br class="">
postdoctoral researcher<br class="">
Experimental Psychology Department<br class="">
Ghent University<o:p class=""></o:p></span></div>
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<span lang="en-BE" class=""><o:p class=""> </o:p></span></div>
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