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Hi Jane,
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<div class="">Yes, it is not a problem (and expected) for the outside-of-the-head dipole positions to be represented by NaNs.</div>
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<div class="">Best wishes and keep up the good work,</div>
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<div class="">Jan-Mathijs</div>
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J.M.Schoffelen, MD PhD<br class="">
Senior Researcher, VIDI-fellow - PI, language in interaction<br class="">
Telephone: +31-24-3614793</div>
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Physical location: room 00.028</div>
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Donders Centre for Cognitive Neuroimaging, Nijmegen, The Netherlands<br class="">
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<div class="">On 11 Feb 2019, at 03:42, Jane Tan <<a href="mailto:Jane.Tan@murdoch.edu.au" class="">Jane.Tan@murdoch.edu.au</a>> wrote:</div>
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<div style="margin-top: 0px; margin-bottom: 0px;" class="">Dear Fieldtrippers,</div>
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<div style="margin-top: 0px; margin-bottom: 0px;" class="">My name is Jane, and this is my first time posting a question to this community. First and foremost, I would like to apologise if my question is extremely trivial.</div>
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<div style="margin-top: 0px; margin-bottom: 0px;" class="">I am trying to perform source analysis using the Beamformer DICS approach on EEG data. My data has been preprocessed using the EEGlab toolbox, and in this particular instance I am only interested in
20 Hz source space activity for time periods around event 'SS'.</div>
<div style="margin-top: 0px; margin-bottom: 0px;" class="">My question is very simple: is it normal to get NaNs in the avg.pow output of the source analysis? It seems to make sense; positions in the grid that do not have 20 Hz reconstructed activity would be
represented by NaNs. However, I'm a novice (in all aspects: Matlab, EEG analysis, and Fieldtrip), and for a sanity check, would like to confirm that getting NaNs in my source output is normal.</div>
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<div style="margin-top: 0px; margin-bottom: 0px;" class="">Here is the code I executed:</div>
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<div class="">%% Loading data</div>
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<div class="">[EEG] = pop_loadset('E:\preprocesseddata.set'); </div>
<div class="">EEG = pop_epoch( EEG, { 'SSstopDIN' }, [-0.5 0.8]); % Epoch to event of interest 'SS'</div>
<div class="">dataSS_epoched = eeglab2fieldtrip(EEG, 'preprocessing'); % Convert eeglab dataset to fieldtrip data</div>
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<div class="">%% Calculating the cross spectral density matrix</div>
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<div class=""> cfg = [];</div>
<div class=""> cfg.method = 'mtmfft';</div>
<div class=""> cfg.taper = 'hanning';</div>
<div class=""> cfg.output = 'powandcsd';</div>
<div class=""> cfg.foi = 4:40;</div>
<div class=""> cfg.keeptrials = 'no';</div>
<div class=""> TFR_SS = ft_freqanalysis(cfg, dataSS_epoched);</div>
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<div class="">%% Computing leadfield </div>
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<div class="">cfg = [];</div>
<div class="">cfg.headmodel = vol; %using BEM head model from FT template</div>
<div class="">cfg.reducerank = 3; %default = 3 for EEG</div>
<div class="">cfg.elec = elec_aligned </div>
<div class="">cfg.grid.resolution = 0.5; % use a 3-D grid with a 0.5 cm resolution</div>
<div class="">cfg.grid.unit = 'cm';</div>
<div class="">[grid] = ft_prepare_leadfield(cfg);</div>
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<div class="">%% Source analysis (without contrasts; just testing source reconstruction for one condition first)</div>
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<div class="">cfg = [];</div>
<div class="">cfg.method = 'dics';</div>
<div class="">cfg.frequency = 20; </div>
<div class="">cfg.grid = grid;</div>
<div class="">cfg.headmodel = vol;</div>
<div class="">cfg.elec = elec_aligned;</div>
<div class="">sourceSS_nocon = ft_sourceanalysis(cfg, TFR_SS); % Here, <span style="font-family: Calibri, Helvetica, sans-serif, EmojiFont, 'Apple Color Emoji', 'Segoe UI Emoji', NotoColorEmoji, 'Segoe UI Symbol', 'Android Emoji', EmojiSymbols; font-size: 16px;" class="">sourceSS_nocon.avg.pow
shows NaNs in some cells and not others</span></div>
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<div style="margin-top: 0px; margin-bottom: 0px;" class="">My sincere thanks in advance!</div>
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<div style="margin-top: 0px; margin-bottom: 0px;" class=""><span lang="EN-SG" class="">Best regards,</span></div>
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<div style="margin-top: 0px; margin-bottom: 0px;" class=""><span lang="EN-SG" class="">Jane</span></div>
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<b class=""><span style="font-size: 9pt; font-family: Verdana, sans-serif;" class="">Jane Tan, PhD Candidate</span></b><span style="font-size: 11.5pt; font-family: 'Segoe UI', sans-serif; color: rgb(33, 33, 33);" class=""></span></p>
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<b class=""><span style="font-size: 9pt; font-family: Verdana, sans-serif;" class="">School of Psychology and Exercise Science | Murdoch University</span></b><span style="font-size: 11.5pt; font-family: 'Segoe UI', sans-serif; color: rgb(33, 33, 33);" class=""></span></p>
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<span style="font-size: 9pt; font-family: Verdana, sans-serif;" class="">Room 3.001 Social Science Building, 90 South Street, Murdoch WA 6150 <b class="">| </b>Email:<span class="Apple-converted-space"> </span><a href="mailto:Jane.Tan@murdoch.edu.au" class="">Jane.Tan@murdoch.edu.au</a></span><span style="font-size: 11.5pt; font-family: 'Segoe UI', sans-serif; color: rgb(33, 33, 33);" class=""></span></p>
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<a href="https://doi.org/10.1371/journal.pcbi.1002202" style="font-family: Helvetica; font-size: 14px; font-style: normal; font-variant-caps: normal; font-weight: normal; letter-spacing: normal; orphans: auto; text-align: start; text-indent: 0px; text-transform: none; white-space: normal; widows: auto; word-spacing: 0px; -webkit-text-size-adjust: auto; -webkit-text-stroke-width: 0px;" class="">https://doi.org/10.1371/journal.pcbi.1002202</a></div>
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