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This section covers all aspects of MEG data analysis. The following pages assume that you have [[afni.nimh.nih.gov|AFNI]] installed and have a reasonably good idea of how to use it.
This section covers all aspects of MEG data analysis. The following pages assume that you have [[afni.nimh.nih.gov|AFNI]] installed and have a reasonably good idea of how to use it.


* [[CTF DataEditor|Viewing raw data: the CTF Data Editor]]
* [[CTF Tools|The CTF Tools in a Singularity Container]]


* [[Pyctf|Accessing CTF Datasets from Python]]
* [[Dataset and Task Utilities|Preprocessing: Dataset and Task Utilities]]


* [[Time Frequency Analysis|Time Frequency Analysis Tools]]
* [[Time Frequency Analysis|Time Frequency Analysis Tools]]
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* [[Fun Stuff - How to make a movie| Fun Stuff - How to make a movie ]]
* [[Fun Stuff - How to make a movie| Fun Stuff - How to make a movie ]]


* [[External MEG Analysis Toolboxes | Other Software packages]]
* [[MEG analysis on Biowulf| MEG analysis on Biowulf]]


* [[Mne bids pipeline | MNE Bids Pipeline and BIDS background]]
==== MEG Core pyctf tools ported to Python 3 ====
pyctf tools are a collection of Python scripts useful in the analysis of data sets collected from the CTF scanner.
These tools have been rewritten using modern Python 3 syntax following standard coding conventions. Most of these programs will run unmodified under MacOS, Windows, and the various versions of Linux with a Python 3.4 distribution or later installed. Python programs requiring modules not included in the standard Python library are indicated.


* [[External MEG Analysis Toolboxes | Other Software packages]]
* [https://megcore.nih.gov/pyctf/parsemarks.py parsemarks.py download]

<syntaxhighlight lang="bash">
parsemarks.py
usage: parsemarks.py [-h] [-l] dataset

Extract the marks from the marker file associated with dataset
and print themin a useful format.

positional arguments:
dataset path to CTF dataset or MarkerFile.mrk (required)

optional arguments:
-h, --help show this help message and exit
-l the marks are labeled in the output. Useful for debugging.
</syntaxhighlight>

parsemarks2.py provides provides more control over the output.

* [https://megcore.nih.gov/pyctf/parsemarks2.py parsemarks2.py download]

<syntaxhighlight lang="bash">
parsemarks2.py
usage: parsemarks2.py [-h] [-l] [-m marker...] [-s] dataset


* Converting Brainsight Localizers or AFNI fiducials [https://github.com/nih-megcore/nih_to_mne to tags or MNE transforms]
Extract the marks from the marker file associated with dataset and print them
in a useful format.


* Connectivity Analysis [[Connectivity Resources | Resources]]
positional arguments:
dataset path to CTF dataset (required)


* ICA Cleaning/Analysis [[ICA cleaning | ICA]]
optional arguments:
-h, --help show this help message and exit
-l the marks are labeled in the output. Useful for debugging.
-m marker the specified mark(s) is reported (default is all markers)
-s shows a list of markers in dataset and exits
</syntaxhighlight>


* Github MEG Code for NIH Labs doing MEG research [[NIH Labs Github Pages | Github Links]]
* [https://megcore.nih.gov/pyctf/parsemarks_report.py parsemarks_report.py download]
: Requires the ''xlsxwriter'' module for writing Excel files.


==== Basic Tutorials for New/Inexperienced Researchers====
<syntaxhighlight lang="bash">
parsemarks_report.py
usage: parsemarks_report.py [-h] [-v] studydir


Most MEG Core scripts are written in bash, which is the command line interface to Linux. Python is a more powerful programming language, and Tom and Jeff have written some of our scripts in Python, but you don't really need to know how they work, just what they do. The AFNI tutorial below is geared towards fMRI/BOLD analysis. For MEG analysis you can ignore all the BOLD things, we just use it for statistics and display of the final source reconstruction results. If you want to go more in depth, Tom can explain the ones we use. Biowulf can be employed later when you want it all to go faster.
Reports on the marker set from every dataset directory under a study
directory. MEG studies consists of a collection of datasets, each with its own
MarkerFile.mrk, organized under a top level (studydir) directory. Output is an
excel file stored in your ~/excel folder.


* HPC: Introduction to Linux -- https://hpc.nih.gov/training/handouts/Introduction_to_Linux.pdf
positional arguments:
* HPC: Bash Class -- https://hpc.nih.gov/training/bash_class/
studydir path to a toplevel directory holding a set of dataset
* Python Tutorial -- https://docs.python.org/3/tutorial/
directories (required)
* AFNI Introduction -- https://andysbrainbook.readthedocs.io/en/latest/AFNI/AFNI_Short_Course/AFNI_fMRI_Intro.html
* HPC: Introduction to Biowulf -- https://hpc.nih.gov/training/intro_biowulf


optional arguments:
-h, --help show this help message and exit
</syntaxhighlight>


==== Stimulus Presentation Software ====
==== Stimulus Presentation Software ====

Latest revision as of 12:20, 25 October 2022

MEG Data Analysis

This section covers all aspects of MEG data analysis. The following pages assume that you have AFNI installed and have a reasonably good idea of how to use it.

  • ICA Cleaning/Analysis ICA

Basic Tutorials for New/Inexperienced Researchers

Most MEG Core scripts are written in bash, which is the command line interface to Linux. Python is a more powerful programming language, and Tom and Jeff have written some of our scripts in Python, but you don't really need to know how they work, just what they do. The AFNI tutorial below is geared towards fMRI/BOLD analysis. For MEG analysis you can ignore all the BOLD things, we just use it for statistics and display of the final source reconstruction results. If you want to go more in depth, Tom can explain the ones we use. Biowulf can be employed later when you want it all to go faster.


Stimulus Presentation Software

PsychoPy: Psychology software in Python PsychoPy is an open-source application that allows you to run a wide range of neuroscience, psychology and psychophysics experiments. It’s a free, powerful alternative to Presentation™ or to e-Prime™, written in Python (a free alternative to Matlab™ ).

Presentation: NeuroBehavioral Systems (NBS), Inc. Presentation® is a stimulus delivery and experiment control program for neuroscience written for Microsoft Windows.

E-prime 3: Psychology Software Tools E-Prime® 3.0 software for behavioral research. Build your own experiments using E-Prime’s easy-to-use graphical interface. Design, collect, and analyze data – all within a few hours!

Miscellaneous Documentation

  • SensLayout-275 — a color picture showing the sensor names and relative locations (ps).
  • SensLayout-275 - a color picture showing the sensor names and relative locations (pdf).
  • File Formats - CTF MEG data file format
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