<html><head></head><body style="word-wrap: break-word; -webkit-nbsp-mode: space; -webkit-line-break: after-white-space; ">thanks I have been using this example as a base but I was curious about the following:<div>1. can <span class="Apple-style-span" style="font-family: Consolas, 'Deja Vu Sans Mono', 'Bitstream Vera Sans Mono', monospace; font-size: 15px; line-height: 15px; white-space: pre; "><a href="http://martinos.org/mne/stable/generated/mne.SourceEstimate.html#mne.SourceEstimate" style="color: inherit; text-decoration: underline; "><span class="n">stc</span></a></span><span class="Apple-style-span" style="font-family: Consolas, 'Deja Vu Sans Mono', 'Bitstream Vera Sans Mono', monospace; font-size: 15px; line-height: 15px; white-space: pre; "><span class="o" style="color: rgb(102, 102, 102); ">.</span></span><span class="Apple-style-span" style="font-family: Consolas, 'Deja Vu Sans Mono', 'Bitstream Vera Sans Mono', monospace; font-size: 15px; line-height: 15px; white-space: pre; "><span class="n">extract_label_time_course </span></span> work on a epoched data . fif file containing all my event condtions</div><div>2. lets say I want to extract as a time course just the vertices with the center of mass or perhaps an roi reflecting this vertices and a small number of neighboring vertices instead of the mean of a whole anatomic roi label. how could I do that?</div><div>3 ) with np.save can I also ad a vector that corresponds to the event type that each epoch represents?</div><div><div><br></div><div>thanks //dave</div></div><div><div>On Jan 13, 2014, at 5:43 PM, Alexandre Gramfort wrote:</div><br class="Apple-interchange-newline"><blockquote type="cite"><div>hi David,<br><br>have a look at:<br><br><a href="http://martinos.org/mne/stable/auto_examples/inverse/plot_label_source_activations.html">http://martinos.org/mne/stable/auto_examples/inverse/plot_label_source_activations.html</a><br><br>extract_label_time_course will give you a time series per stc, then you can make<br>an array from all time courses and save it as a numpy binary file with np.save<br><br>let me know if you need more help<br><br>HTH<br>Alex<br><br><br>On Mon, Jan 13, 2014 at 7:58 PM, David Leitman<br><leitman@mail.med.upenn.edu> wrote:<br><blockquote type="cite">Hi all,<br></blockquote><blockquote type="cite"> I am trying to extract from a structural roi a time series source estimate for each one of the epoch in each of my conditions<br></blockquote><blockquote type="cite"><br></blockquote><blockquote type="cite">first I read in the saved epoched data fif file:<br></blockquote><blockquote type="cite"><br></blockquote><blockquote type="cite"> epochs=mne.read_epochs(fname_epoched)<br></blockquote><blockquote type="cite"><br></blockquote><blockquote type="cite">next I apply the inverse operator that I created using the evoked data and the anatomic label previously created and execute the following:<br></blockquote><blockquote type="cite"><br></blockquote><blockquote type="cite">stcs = apply_inverse_epochs(epochs,inverse_operator, lambda2, method, anat_label1,<br></blockquote><blockquote type="cite"> pick_ori="normal")<br></blockquote><blockquote type="cite"><br></blockquote><blockquote type="cite">what I would like to do next is calculate the mean activation within this roi comprising 480 vertices. so that I have one average value for each of my epochs and conditions and save an output matrix comprising time series for each epoch and including a vector for event id<br></blockquote><blockquote type="cite"><br></blockquote><blockquote type="cite">I tried picking up from the a similar stage in the plot_compute_mne_inverse_epochs_in_label.py substituting my commands above for<br></blockquote><blockquote type="cite"><br></blockquote><blockquote type="cite"># Read epochs<br></blockquote><blockquote type="cite">epochs = mne.Epochs(raw, events, event_id, tmin, tmax, picks=picks,<br></blockquote><blockquote type="cite"> baseline=(None, 0), reject=dict(mag=4e-12, grad=4000e-13,<br></blockquote><blockquote type="cite"> eog=150e-6))<br></blockquote><blockquote type="cite"><br></blockquote><blockquote type="cite"># Compute inverse solution and stcs for each epoch<br></blockquote><blockquote type="cite">stcs = apply_inverse_epochs(epochs, inverse_operator, lambda2, method, label,<br></blockquote><blockquote type="cite"> pick_ori="normal")<br></blockquote><blockquote type="cite"><br></blockquote><blockquote type="cite">mean_stc = sum(stcs) / len(stcs)<br></blockquote><blockquote type="cite"><br></blockquote><blockquote type="cite"><br></blockquote><blockquote type="cite">but mean_stc is not what I want and stcs does not have attribute save like for example stc.save<br></blockquote><blockquote type="cite"><br></blockquote><blockquote type="cite">any help would be appreciated<br></blockquote><blockquote type="cite">thanks dave<br></blockquote><blockquote type="cite"><br></blockquote><blockquote type="cite"><br></blockquote><blockquote type="cite"><br></blockquote><blockquote type="cite"><br></blockquote><blockquote type="cite">_______________________________________________<br></blockquote><blockquote type="cite">Mne_analysis mailing list<br></blockquote><blockquote type="cite">Mne_analysis@nmr.mgh.harvard.edu<br></blockquote><blockquote type="cite">https://mail.nmr.mgh.harvard.edu/mailman/listinfo/mne_analysis<br></blockquote><blockquote type="cite"><br></blockquote><blockquote type="cite"><br></blockquote><blockquote type="cite">The information in this e-mail is intended only for the person to whom it is<br></blockquote><blockquote type="cite">addressed. 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-webkit-text-decorations-in-effect: none; -webkit-text-size-adjust: auto; -webkit-text-stroke-width: 0px; "><div style="word-wrap: break-word; -webkit-nbsp-mode: space; -webkit-line-break: after-white-space; "><span class="Apple-style-span" style="border-collapse: separate; color: rgb(0, 0, 0); font-variant: normal; letter-spacing: normal; line-height: normal; orphans: 2; text-indent: 0px; text-transform: none; white-space: normal; widows: 2; word-spacing: 0px; -webkit-border-horizontal-spacing: 0px; -webkit-border-vertical-spacing: 0px; -webkit-text-decorations-in-effect: none; -webkit-text-size-adjust: auto; -webkit-text-stroke-width: 0px; "><div style="word-wrap: break-word; -webkit-nbsp-mode: space; -webkit-line-break: after-white-space; "><div><div style="font-weight: normal; font-style: normal; font-family: Helvetica; font-size: 16px; "><font class="Apple-style-span" face="'Lucida Grande'" size="2"><span class="Apple-style-span" style="font-size: 10px; "><br></span></font></div></div></div></span><br class="Apple-interchange-newline"></div></span><br class="Apple-interchange-newline"></div></span><br class="Apple-interchange-newline"></div></span><br class="Apple-interchange-newline"></div></span><br class="Apple-interchange-newline"></div></span><br class="Apple-interchange-newline"></div></span><br class="Apple-interchange-newline"></div></span><br class="Apple-interchange-newline"></div></span><br class="Apple-interchange-newline"></div></span><br class="Apple-interchange-newline"></div></span><br class="Apple-interchange-newline"></div></span><br class="Apple-interchange-newline"></span><br class="Apple-interchange-newline">
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