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Dataset Open Access

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

!!IMPORTANT!!

Please be aware that the MRI segmetations for subjects, 1, 18 and 20 are not correct. Therefore, files for these subjects should be retrieved from version 2. All files for the remaining subjects can be retrieved here. Scripts sould be retrieved from: https://github.com/ualsbombe/omission_frontiers.git

!!IMPORTANT!!

 

!!Please go to http://natmeg.se/teamNatMEG/lau%20andersen.html to get the preprinted manuscripts associated with the data!!

Dataset with tactile expectations, meant to be analysed with pipelines for either MNE-python or FieldTrip, aiming to follow the MEG-BIDS structure
Please refer to (hopefully) upcoming manuscripts in Frontiers for the research topic called From raw MEG/EEG to publication: how to perform MEG/EEG group analysis with free academic software. The articles will be authored by Lau M. Andersen. Manuscripts showcasing the pipeline may also be requested by emailing me at lau.moller.andersen@ki.se or ualsbombe@gmail.com

##################
## The MEG data ##
##################

Raw fif files are contained in the data folder, ordered by subject (n=20)
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

########################
## Processed MRI data ##
########################

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' identity

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
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) specifying the co-registration between MEG and MRI coordinate systems for the MNE-Python analysis

######################
## Metadata ##########
######################

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

*scans_tsv contain information about the scans conducted

###################
## Scripts ########
###################

In the scripts folder there are three folders: "matlab", "python" and "bash". They each contain scripts of that type.
The matlab scripts are for the FieldTrip analysis
The python and bash scripts are for the MNE-Python analysis

Files (89.5 GB)
Name Size
FreeSurfer.zip
md5:f819c5b3969bc83d1ca742e8c8c1cc1f
2.3 GB Download
README.txt
md5:727057e2516309897cf3ce34b3686071
5.0 kB Download
scripts.zip
md5:823c344753213fd5a2764d3cdbae7d9f
82.7 kB Download
sub-01.zip
md5:6f4c61d96bce0d0fb5335f2cb020fa10
4.4 GB Download
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sub-20.zip
md5:df73a5599738d3d51048b76f1317eb78
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