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<div style="direction: ltr;">Thanks Alex and Mainak,</div>
<div><br>
</div>
<div style="direction: ltr;"> I’ll try changing the number of components and interpolating a larger section of the data to abolish the stim artifact (the data I sent was TMS-EEG data). </div>
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<div style="direction: ltr;">Alex</div>
<div><br>
</div>
<div style="direction: ltr;">Translational NeuroEngineering Laboratory</div>
<div style="direction: ltr;">Division of Neurotherapeutics, Department of Psychiatry</div>
<div style="direction: ltr;">Massachusetts General Hospital, Martinos Center</div>
<div style="direction: ltr;">149 13th St Charlestown #2301, Boston, MA 02129</div>
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<div id="divRplyFwdMsg" dir="dir="ltr""><font face="Calibri, sans-serif" style="font-size:11pt" color="#000000"><b>From:</b> mne_analysis-bounces@nmr.mgh.harvard.edu <mne_analysis-bounces@nmr.mgh.harvard.edu> on behalf of Mainak Jas <mainakjas@gmail.com><br>
<b>Sent:</b> Wednesday, July 3, 2019 4:41 PM<br>
<b>To:</b> Discussion and support forum for the users of MNE Software<br>
<b>Subject:</b> Re: [Mne_analysis] Applying ICA with No Components Selected out but Signal Changes Using Default Parameters
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<p><span style="padding:3px 10px; border-radius:5px; color:#ffffff; font-weight:bold; display:inline-block; background-color:#ff0000"> External Email - Use Caution </span></p>
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<p style="margin:0px 0px 1.2em!important">I can confirm what Alex said. Here’s a simpler script to play with:</p>
<pre style="font-size:0.85em; font-family:Consolas,Inconsolata,Courier,monospace; font-size:1em; line-height:1.2em; margin:1.2em 0px"><code class="hljs language-py" style="font-size:0.85em; font-family:Consolas,Inconsolata,Courier,monospace; margin:0px 0.15em; padding:0px 0.3em; white-space:pre-wrap; border:1px solid rgb(234,234,234); background-color:rgb(248,248,248); border-radius:3px; display:inline; white-space:pre; overflow:auto; border-radius:3px; border:1px solid rgb(204,204,204); padding:0.5em 0.7em; display:block!important; display:block; overflow-x:auto; padding:0.5em; color:rgb(51,51,51); background:rgb(248,248,248) none repeat scroll 0% 0%"><span class="hljs-keyword" style="color:rgb(51,51,51); font-weight:bold">import</span> mne<span class="hljs-keyword" style="color:rgb(51,51,51); font-weight:bold">from</span> mne.preprocessing <span class="hljs-keyword" style="color:rgb(51,51,51); font-weight:bold">import</span> ICA<span class="hljs-keyword" style="color:rgb(51,51,51); font-weight:bold">from</span> numpy.testing <span class="hljs-keyword" style="color:rgb(51,51,51); font-weight:bold">import</span> assert_array_equalepochs = mne.read_epochs(<span class="hljs-string" style="color:rgb(221,17,68)">'test-epo.fif'</span>, preload=<span class="hljs-keyword" style="color:rgb(51,51,51); font-weight:bold">True</span>)epochs.pick_types(meg=<span class="hljs-keyword" style="color:rgb(51,51,51); font-weight:bold">False</span>, eeg=<span class="hljs-keyword" style="color:rgb(51,51,51); font-weight:bold">True</span>)epochs.crop(<span class="hljs-number" style="color:rgb(0,128,128)">0.2</span>, <span class="hljs-keyword" style="color:rgb(51,51,51); font-weight:bold">None</span>)epochs2 = epochs.copy()reject = dict(eeg=<span class="hljs-number" style="color:rgb(0,128,128)">200e-6</span>)ica = ICA(method=<span class="hljs-string" style="color:rgb(221,17,68)">'fastica'</span>, random_state=<span class="hljs-number" style="color:rgb(0,128,128)">42</span>, max_iter=<span class="hljs-number" style="color:rgb(0,128,128)">500</span>, n_components=<span class="hljs-number" style="color:rgb(0,128,128)">0.95</span>)ica.fit(epochs.copy().drop_bad(reject=reject), decim=<span class="hljs-number" style="color:rgb(0,128,128)">10</span>)ica.apply(epochs2, exclude=[])epochs.average().plot()epochs2.average().plot()<span class="hljs-comment" style="color:rgb(153,153,136); font-style:italic"># from mne import combine_evoked</span><span class="hljs-comment" style="color:rgb(153,153,136); font-style:italic"># evoked_diff = combine_evoked([epochs.average(), -epochs2.average()], 'equal')</span><span class="hljs-comment" style="color:rgb(153,153,136); font-style:italic"># evoked_diff.plot()</span>assert_array_equal(epochs.get_data(), epochs2.get_data())</code></pre>
<p style="margin:0px 0px 1.2em!important">Using <code style="font-size:0.85em; font-family:Consolas,Inconsolata,Courier,monospace; margin:0px 0.15em; padding:0px 0.3em; white-space:pre-wrap; border:1px solid rgb(234,234,234); background-color:rgb(248,248,248); border-radius:3px; display:inline">
n_components=0.95</code> vs <code style="font-size:0.85em; font-family:Consolas,Inconsolata,Courier,monospace; margin:0px 0.15em; padding:0px 0.3em; white-space:pre-wrap; border:1px solid rgb(234,234,234); background-color:rgb(248,248,248); border-radius:3px; display:inline">
n_components=1.0</code> actually seems to make a huge difference, perhaps for the reasons that Alex outlines. Ultimately, what you reconstruct depends on the quality of your decomposition -- and it's not perfect.<br>
</p>
<p style="margin:0px 0px 1.2em!important">Best,<br>
Mainak</p>
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<div class="gmail_quote">
<div dir="ltr" class="gmail_attr">On Wed, Jul 3, 2019 at 4:23 PM Alexandre Gramfort <<a href="mailto:alexandre.gramfort@inria.fr">alexandre.gramfort@inria.fr</a>> wrote:<br>
</div>
<blockquote class="gmail_quote" style="margin:0px 0px 0px 0.8ex; border-left:1px solid rgb(204,204,204); padding-left:1ex">
External Email - Use Caution <br>
<br>
Hi Alex,<br>
<br>
I could replicate the pb and start to investigate.<br>
<br>
You have a strong transient artifact in your data (stim artifact I suspect)<br>
and this affects the conditioning of the mixing matrix. So when computing<br>
the pinv here: <a href="https://github.com/mne-tools/mne-python/blob/master/mne/preprocessing/ica.py#L677" rel="noreferrer" target="_blank">
https://github.com/mne-tools/mne-python/blob/master/mne/preprocessing/ica.py#L677</a><br>
the mixing is not numerically the inverse of the unmixing matrix.<br>
<br>
I don't know exactly how to fix this but that's a starting point.<br>
He someone can look into this it's great.<br>
<br>
Alex<br>
<br>
On Wed, Jul 3, 2019 at 3:33 PM Rockhill, Alexander P.<br>
<<a href="mailto:AROCKHILL@mgh.harvard.edu" target="_blank">AROCKHILL@mgh.harvard.edu</a>> wrote:<br>
><br>
> Hi Alex and Mainak,<br>
><br>
> The n_components argument is given None which yields 57 components and there are 64 channels with 7 bad channels which are not included so no I don't think it's because of the dimensionality reduction. Maybe it's some whitening.<br>
><br>
> To see something similar to what I'm looking at as far as scaling you can use the script below but I haven't been able to replicate the changes after ICA with sample data. I filedropped you both test epochs to the emails you responded to the thread with that
does show that.<br>
><br>
> from time import time<br>
> import matplotlib.pyplot as plt<br>
> import mne<br>
> from mne.preprocessing import ICA<br>
> from mne.datasets import sample<br>
><br>
> '''data_path = sample.data_path()<br>
> raw_fname = data_path + '/MEG/sample/sample_audvis-raw.fif'<br>
><br>
> raw = mne.io.Raw(raw_fname, preload=True)<br>
> events = mne.find_events(raw)<br>
> epochs = mne.Epochs(raw, events, preload=True)<br>
><br>
> '''<br>
> epochs = mne.read_epochs('test-epo.fif', preload=True)<br>
><br>
> epochs = epochs.pick_types(meg=False, eeg=True)<br>
><br>
> fig, (ax0, ax1) = plt.subplots(1,2)<br>
> epochs.average().plot(axes=ax0, show=False)<br>
><br>
> ica = ICA(method='fastica', random_state=0)<br>
> t0 = time()<br>
> ica.fit(epochs)<br>
> fit_time = time() - t0<br>
> epochs = ica.apply(epochs, exclude=ica.exclude)<br>
> epochs.average().plot(axes=ax1, show=False)<br>
> ica.plot_sources(epochs)<br>
><br>
> Thanks,<br>
><br>
> Alex<br>
><br>
> Translational NeuroEngineering Laboratory<br>
> Division of Neurotherapeutics, Department of Psychiatry<br>
> Massachusetts General Hospital, Martinos Center<br>
> 149 13th St Charlestown #2301, Boston, MA 02129<br>
> ________________________________<br>
> From: <a href="mailto:mne_analysis-bounces@nmr.mgh.harvard.edu" target="_blank">
mne_analysis-bounces@nmr.mgh.harvard.edu</a> <<a href="mailto:mne_analysis-bounces@nmr.mgh.harvard.edu" target="_blank">mne_analysis-bounces@nmr.mgh.harvard.edu</a>> on behalf of Alexandre Gramfort <<a href="mailto:alexandre.gramfort@inria.fr" target="_blank">alexandre.gramfort@inria.fr</a>><br>
> Sent: Wednesday, July 3, 2019 2:30 AM<br>
> To: Discussion and support forum for the users of MNE Software<br>
> Subject: Re: [Mne_analysis] Applying ICA with No Components Selected out but Signal Changes Using Default Parameters<br>
><br>
><br>
> External Email - Use Caution<br>
><br>
> hi,<br>
><br>
> can you check that the number of components you fit is equal to the number of channels?<br>
> If it's less you have a dimensionality reduction step.<br>
><br>
> Alex<br>
><br>
><br>
> On Tue, Jul 2, 2019 at 11:28 PM Mainak Jas <<a href="mailto:mainakjas@gmail.com" target="_blank">mainakjas@gmail.com</a>> wrote:<br>
><br>
> External Email - Use Caution<br>
><br>
> Hi Alex,<br>
><br>
> Could you provide us a full script on the MNE sample data that we can run?<br>
><br>
> Mainak<br>
><br>
> On Tue, Jul 2, 2019 at 5:14 PM Rockhill, Alexander P. <<a href="mailto:AROCKHILL@mgh.harvard.edu" target="_blank">AROCKHILL@mgh.harvard.edu</a>> wrote:<br>
><br>
> Also, of note the ica scale is off by quite a lot in the plot_sources plot, it is way too zoomed in.<br>
><br>
> Alex<br>
><br>
> Translational NeuroEngineering Laboratory<br>
> Division of Neurotherapeutics, Department of Psychiatry<br>
> Massachusetts General Hospital, Martinos Center<br>
> 149 13th St Charlestown #2301, Boston, MA 02129<br>
> ________________________________<br>
> From: <a href="mailto:mne_analysis-bounces@nmr.mgh.harvard.edu" target="_blank">
mne_analysis-bounces@nmr.mgh.harvard.edu</a> <<a href="mailto:mne_analysis-bounces@nmr.mgh.harvard.edu" target="_blank">mne_analysis-bounces@nmr.mgh.harvard.edu</a>> on behalf of Rockhill, Alexander P. <<a href="mailto:AROCKHILL@mgh.harvard.edu" target="_blank">AROCKHILL@mgh.harvard.edu</a>><br>
> Sent: Tuesday, July 2, 2019 3:25 PM<br>
> To: <a href="mailto:mne_analysis@nmr.mgh.harvard.edu" target="_blank">mne_analysis@nmr.mgh.harvard.edu</a><br>
> Subject: [Mne_analysis] Applying ICA with No Components Selected out but Signal Changes Using Default Parameters<br>
><br>
> Hi,<br>
><br>
> In an analysis, I am running:<br>
><br>
> ica = ICA(method='fastica', n_components=n_components, # n_components=None<br>
> random_state=seed)<br>
> ica.fit(inst2)<br>
> ...<br>
> inst2 = ica.apply(inst2, exclude=ica.exclude)<br>
><br>
> and when I skip all intermediate steps and just fit the ICA and apply it with an empty list for ica.exclude the signal still changes, quite a bit. I thought if no components were selected out and all the max PCA components were used the signal would be
unchanged or basically unchanged. Is this a bug or something with my implementation?<br>
><br>
> Thanks,<br>
><br>
> Alex<br>
><br>
> Translational NeuroEngineering Laboratory<br>
> Division of Neurotherapeutics, Department of Psychiatry<br>
> Massachusetts General Hospital, Martinos Center<br>
> 149 13th St Charlestown #2301, Boston, MA 02129<br>
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