Dataset Open Access

# Somatosensory data for group analyses in the Frontiers Reseach Topic: From raw MEG/EEG to publication: how to perform MEG/EEG group analysis with free academic software.

Lau Møller Andersen

### DataCite XML Export

<?xml version='1.0' encoding='utf-8'?>
<identifier identifierType="DOI">10.5281/zenodo.1134776</identifier>
<creators>
<creator>
<creatorName>Lau Møller Andersen</creatorName>
<affiliation>CFIN, Aarhus University</affiliation>
</creator>
</creators>
<titles>
<title>Somatosensory data for group analyses in the Frontiers Reseach Topic: From raw MEG/EEG to publication: how to perform MEG/EEG group analysis with free academic software.</title>
</titles>
<publisher>Zenodo</publisher>
<publicationYear>2017</publicationYear>
<subjects>
<subject>MEG, analysis pipeline, MNE-Python, minimum norm estimate (MNE), Tactile Expectations, Group analysis, good practice, fieldtrip, beamformer</subject>
</subjects>
<dates>
<date dateType="Issued">2017-10-01</date>
</dates>
<resourceType resourceTypeGeneral="Dataset"/>
<alternateIdentifiers>
<alternateIdentifier alternateIdentifierType="url">https://zenodo.org/record/1134776</alternateIdentifier>
</alternateIdentifiers>
<relatedIdentifiers>
<relatedIdentifier relatedIdentifierType="DOI" relationType="IsVersionOf">10.5281/zenodo.998517</relatedIdentifier>
</relatedIdentifiers>
<rightsList>
<rights rightsURI="info:eu-repo/semantics/openAccess">Open Access</rights>
</rightsList>
<descriptions>
<description descriptionType="Abstract">&lt;p&gt;&lt;strong&gt;If you use the data or the analysis pipeline, please refer to:&lt;/strong&gt;&lt;/p&gt;

&lt;p&gt;Andersen, L.M., 2018. Group Analysis in MNE-Python of Evoked Responses from a Tactile Stimulation Paradigm: A Pipeline for Reproducibility at Every Step of Processing, Going from Individual Sensor Space Representations to an across-Group Source Space Representation. Front. Neurosci. 12. &lt;a href="https://doi.org/10.3389/fnins.2018.00006"&gt;https://doi.org/10.3389/fnins.2018.00006&lt;/a&gt;&lt;/p&gt;

&lt;p&gt;&lt;strong&gt;and/or&lt;/strong&gt;&lt;/p&gt;

&lt;p&gt;Andersen, L.M., 2018. Group Analysis in FieldTrip of Time-Frequency Responses: A Pipeline for Reproducibility at Every Step of Processing, Going From Individual Sensor Space Representations to an Across-Group Source Space Representation. Front. Neurosci. 12. &lt;a href="https://doi.org/10.3389/fnins.2018.00261"&gt;https://doi.org/10.3389/fnins.2018.00261&lt;/a&gt;&lt;/p&gt;

&lt;p&gt;&lt;strong&gt;IMPORTANT&lt;/strong&gt;&lt;br&gt;
Version 2 only contains subjects 1, 18, 20 and a new version of the FreeSurfer folder. This is due to a (very) wrong co-registration for subject 1 and due to 18 and 20 having had their anatomy files mixed up. This has now been fixed. For all other subjects, please see version 1. Also, get the updated scripts from github instead at: &lt;a href="https://github.com/ualsbombe/omission_frontiers.git"&gt;https://github.com/ualsbombe/omission_frontiers.git&lt;/a&gt;&lt;/p&gt;

&lt;p&gt;&lt;br&gt;
&amp;nbsp;&lt;/p&gt;

&lt;p&gt;Dataset with tactile expectations to be analysed with pipelines for either &lt;a href="https://mne.tools/stable/index.html"&gt;MNE-Python&lt;/a&gt; or &lt;a href="http://www.fieldtriptoolbox.org/"&gt;FieldTrip&lt;/a&gt;, aiming to follow the MEG-BIDS structure&lt;/p&gt;

&lt;p&gt;&lt;br&gt;
&lt;strong&gt;Unzipping the data&lt;/strong&gt;&lt;/p&gt;

&lt;p&gt;Data is compressed into twenty-two different zip-files, one for each of the twenty subjects, one for the FreeSurfer data, one for the scripts files . The easiest way to uncompress and prepare the analysis directories is to create a directory in your home folder called &amp;quot;analyses&amp;quot;, which has a sub-directory called &amp;quot;omission_frontiers_BIDS-FieldTrip&amp;quot;, which has a sub-directory called &amp;quot;data&amp;quot;.&lt;br&gt;
Thus, as an example, in my case, I should have the path:&amp;nbsp;&amp;nbsp;&amp;nbsp; /home/lau/analyses/omission_frontiers_BIDS-FieldTrip/data&lt;/p&gt;

&lt;p&gt;&lt;strong&gt;Path:&lt;/strong&gt;&lt;br&gt;
on a Linux system the path would be&amp;nbsp;&amp;nbsp; /home/your_name/analyses/omission_frontiers_BIDS-FieldTrip/data&lt;br&gt;
on a macOS system the path would be&amp;nbsp;&amp;nbsp; /Users/your_name/analyses/omission_frontiers_BIDS-FieldTrip/data&lt;br&gt;
on a Windows system the path would be C:\Users\your_name\analyses\omission_frontiers_BIDS-FieldTrip\data&lt;/p&gt;

&lt;p&gt;&lt;strong&gt;Steps for unzipping:&lt;/strong&gt;&lt;/p&gt;

&lt;p&gt;1. Set up the folder above according to your operating system, following the examples above and substitute &amp;quot;your_name&amp;quot; for your user name.&lt;br&gt;
2. Unzip each of the subject folders into the data folder (sub-01 - sub-20) (/home/your_name/analyses/omission_frontiers_BIDS-FieldTrip/data)&lt;br&gt;
3. Also unzip the FreeSurfer folder into the data folder (/home/your_name/analyses/omission_frontiers_BIDS-FieldTrip/data)&lt;br&gt;
4. Finally, unzip the scripts folder into /home/your_name/analyses/omission_frontiers_BIDS-FieldTrip/&lt;/p&gt;

&lt;p&gt;Now you are ready to run the analyses.&lt;/p&gt;

&lt;p&gt;&lt;br&gt;
&lt;strong&gt;The MEG data&lt;/strong&gt;&lt;/p&gt;

&lt;p&gt;Raw fif files are contained in the data folder, ordered by subject (n=20)&lt;br&gt;
There is one recording for each subject, MaxFiltered, called oddball_absence-tsss-mc_meg.fif. These are split into three files with -1 and -2 being the remainder of the recording&lt;/p&gt;

&lt;p&gt;&lt;strong&gt;Processed MRI data &lt;/strong&gt;&lt;/p&gt;

&lt;p&gt;For the MRI, only the segmented data are provided. This is to sufficient to make the volume conduction model and the source model, while protecting the subjects&amp;#39; identity&lt;/p&gt;

&lt;p&gt;For Fieldtrip, there is an mri_segmented.mat for each subject, which is found in the meg (sic!) folder for each subject. This has been co-registered to the MEG data&lt;br&gt;
For MNE-Python, the FreeSurfer directory should also be used, which contains a folder for each subject that contains surfaces (surf) and boundary element methods models (bem) that are used for source reconstruction in MNE-python. There is also a trans-file for each subject (oddball_absence_dense-trans.fif) in the meg folder specifying the co-registration between MEG and MRI coordinate systems for the MNE-Python analysis. Finally, the FreeSurfer folder also contains the labels for the cortical surface. This is not used in any of the analyses, but are supplied for interested users.&lt;/p&gt;

&lt;p&gt;&lt;br&gt;

&lt;p&gt;Each subject has a number of tsv-files:&lt;br&gt;
*channel.tsv contain information about the channels in that recording&lt;br&gt;
*events.tsv contain information about the events in that recording&lt;br&gt;
removed_trial_indices.tsv contains information about which events were removed manually (NB! this is only used for the FieldTrip analysis)&lt;br&gt;
ica_components.tsv contains information which independent component were removed manually (NB! this is only used for the FieldTrip analysis)&lt;br&gt;
*scans_tsv contain information about the scans conducted&lt;/p&gt;

&lt;p&gt;&lt;br&gt;
&lt;strong&gt;Scripts &lt;/strong&gt;&lt;/p&gt;

&lt;p&gt;Please see Github for the updated scripts at: &lt;a href="https://github.com/ualsbombe/omission_frontiers.git"&gt;https://github.com/ualsbombe/omission_frontiers.git&lt;/a&gt;&lt;/p&gt;</description>
</descriptions>
</resource>

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