<div dir="ltr"><div><div>Hi Ben,<br><br></div>For Python implementation of FASTER, take a look here: <a href="https://github.com/mne-tools/mne-sandbox/pull/12">https://github.com/mne-tools/mne-sandbox/pull/12</a><br><br></div>Mainak<br><div><div><div><div class="gmail_extra"><br><div class="gmail_quote">On Wed, Oct 12, 2016 at 5:02 PM, Ben McCartney <span dir="ltr"><<a target="_blank" href="mailto:bmccartney06@qub.ac.uk">bmccartney06@qub.ac.uk</a>></span> wrote:<br><blockquote style="margin:0px 0px 0px 0.8ex;border-left:1px solid rgb(204,204,204);padding-left:1ex" class="gmail_quote">
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<p>Hi Denis,</p>
<p><br>
</p>
<p>Thanks for the quick reply, so would you say any of those approaches would yield fairly similar results?</p>
<p>We could also certainly be interested in having a look at Python <span>implementations of ADJUST and FASTER if that was convenient to share as we've already done some testing with them.</span></p>
<p><span><br>
</span></p>
<p><span>Ben</span></p>
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<div dir="ltr" id="gmail-m_86634692833192171divRplyFwdMsg"><font style="font-size:11pt" color="#000000" face="Calibri, sans-serif"><b>From:</b> <a target="_blank" href="mailto:mne_analysis-bounces@nmr.mgh.harvard.edu">mne_analysis-bounces@nmr.mgh.<wbr>harvard.edu</a> <<a target="_blank" href="mailto:mne_analysis-bounces@nmr.mgh.harvard.edu">mne_analysis-bounces@nmr.mgh.<wbr>harvard.edu</a>> on behalf of Denis-Alexander Engemann <<a target="_blank" href="mailto:denis.engemann@gmail.com">denis.engemann@gmail.com</a>><br>
<b>Sent:</b> 12 October 2016 14:20:28<br>
<b>To:</b> Discussion and support forum for the users of MNE Software<br>
<b>Subject:</b> Re: [Mne_analysis] Automatic Component Artefact Removal with EEG</font>
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<div class="gmail-m_86634692833192171gmail_msg" dir="ltr">Hi,
<div class="gmail-m_86634692833192171gmail_msg"><br class="gmail-m_86634692833192171gmail_msg">
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<div class="gmail-m_86634692833192171gmail_msg">This is correct. You can also use the corrmap approach to match EOG components. We have some local Python implementations of ADJUST and FASTER that we would not mind sharing if there is interest in this and would facilitate adopting MNE-Python.</div>
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<div class="gmail-m_86634692833192171gmail_msg">Cheers,</div>
<div class="gmail-m_86634692833192171gmail_msg">Denis</div>
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<div class="gmail_quote gmail-m_86634692833192171gmail_msg">
<div class="gmail-m_86634692833192171gmail_msg" dir="ltr">On Wed, Oct 12, 2016 at 2:16 PM Ben McCartney <<a target="_blank" class="gmail-m_86634692833192171gmail_msg" href="mailto:bmccartney06@qub.ac.uk">bmccartney06@qub.ac.uk</a>> wrote:<br class="gmail-m_86634692833192171gmail_msg">
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<blockquote style="margin:0px 0px 0px 0.8ex;border-left:1px solid rgb(204,204,204);padding-left:1ex" class="gmail_quote gmail-m_86634692833192171gmail_msg">
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<div class="gmail-m_86634692833192171gmail_msg">Hello,</div>
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<div class="gmail-m_86634692833192171gmail_msg"><br class="gmail-m_86634692833192171gmail_msg">
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<div class="gmail-m_86634692833192171gmail_msg">We have been using EEGLAB to run preprocessing over our EEG data before analysing it with Python, and recently we began looking into MNE as an alternative for the preprocessing step. We were using ICA to detect and automatically remove
EOG components using<br class="gmail-m_86634692833192171gmail_msg">
EEGLAB's Binica, along with FASTER and ADJUST. From a quick read through the MNE docs it looks like we could get similar behaviour if we ran the ICA in MNE, then used the find_bads_eog or detect_artifacts function to identify noisy components. Is this the<br class="gmail-m_86634692833192171gmail_msg">
correct approach? Note that we do not have any dedicated EOG channels in our data, would either approach work fine just using a forehead electrode location (like Fpz)?</div>
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<div class="gmail-m_86634692833192171gmail_msg">Thanks,</div>
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<div class="gmail-m_86634692833192171gmail_msg">Ben</div>
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