If you use the data or the analysis pipeline, please refer to:
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. https://doi.org/10.3389/fnins.2018.00006
or to:
Andersen, L. M. 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, (2018).
https://10.3389/fnins.2018.00261
This project facilitates group-level analysis in magnetoencephalography (MEG) using the open toolboxes MNE-Python and FieldTrip. The code is accompanied by an article for each of the toolboxes in an upcoming Frontiers Special Issue.
Intended to be compatible with both Python 2.7 and Python 3.x, but hasn't been extensively tested for Python 3. Testing was done with 0.15 of MNE-Python
For instructions and context of the code please refer to the article called: 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
The DOI is: https://doi.org/10.3389/fnins.2018.00006
A similar article for the FieldTrip pipeline is called: 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 The DOI is: https://doi.org/10.3389/fnins.2018.00261
Most tutorials on MEG analysis are at the single-subject level. This is an attempt at remedying that situation by providing a comphehensive collection of functions that can all be accessed from a single pipeline script (MNE-Python) or several pipeline scripts FieldTrip.
No installation is required. Simply download the scripts and get the data at the following DOI:10.5281/zenodo.998518 or URL:https://zenodo.org/record/998518
Marijn van Vliet, added Python 3 support
git: wmvanvliet
CC BY 4.0