Workflow objects for MEG (not yet) and EEG data processing
I work in a lab that analyzes many psychphysical tasks with the same structure: there are trials with stimuli and responses. If this matches the description of what you’re studying, this tool will probably be very helpful. Using MEEGbuddy, I was able to preprocess ~70 subjects for three tasks using both MEG and EEG in three days not counting computation time.
MEEGbuddy uses MNE, autoreject and PCI (noreun on github) for preprocessing mostly but also analysis of MEEG data. TFR psd multitapers, including sleep scoring, and morlet are supported as well as cluster permutation tests and lempel-ziv complexity (connectivity coming soon hopefully). These preprocessing and analysis steps all come with defaults that will run well as is and have lots of the kinks figured out already with the added bonus that they are saved in BIDS structure (or pretty close).
## Installation Instructions 1. clone into a repository 2. run python setup.py install
To run the demo: 1. install jupyter if you haven’t already: pip install jupyter 2. install dependencies: pip install -r requirements.txt OR pip install pandas mne scipy autoreject seaborn matplotlib tqdm joblib nitime pysurfer naturalneighbor mayavi 3. run “jupyter-notebook” from a terminal
This was originally designed in python2 but now is only supported in python3, it will probably work in python2 for a while longer but no promises
## Citation AP. Rockhill (2018). MEEGbuddy. GitHub Repository. https://github.com/alexrockhill/MEEGbuddy.
## Licensing This software is OSI Certified Open Source Software. OSI Certified is a certification mark of the Open Source Initiative.
Copyright (c) 2018, authors of MEEGbuddy All rights reserved.
Redistribution and use in source and binary forms, with or without modification, are permitted provided that the following conditions are met:
- Redistributions of source code must retain the above copyright notice, this list of conditions and the following disclaimer.
- Redistributions in binary form must reproduce the above copyright notice, this list of conditions and the following disclaimer in the documentation and/or other materials provided with the distribution.
- Neither the names of MEEGbuddy authors nor the names of any contributors may be used to endorse or promote products derived from this software without specific prior written permission.
This software is provided by the copyright holders and contributors “as is” and any express or implied warranties, including, but not limited to, the implied warranties of merchantability and fitness for a particular purpose are disclaimed. In no event shall the copyright owner or contributors be liable for any direct, indirect, incidental, special, exemplary, or consequential damages (including, but not limited to, procurement of substitute goods or services; loss of use, data, or profits; or business interruption) however caused and on any theory of liability, whether in contract, strict liability, or tort (including negligence or otherwise) arising in any way out of the use of this software, even if advised of the possibility of such damage.
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|Filename, size & hash SHA256 hash help||File type||Python version||Upload date|
|MEEGbuddy-0.0-py3.6.egg (150.9 kB) Copy SHA256 hash SHA256||Egg||3.6|
|MEEGbuddy-0.0-py3.7.egg (82.1 kB) Copy SHA256 hash SHA256||Egg||3.7|
|MEEGbuddy-0.0-py3-none-any.whl (99.6 kB) Copy SHA256 hash SHA256||Wheel||py3|
|MEEGbuddy-0.0.tar.gz (44.0 kB) Copy SHA256 hash SHA256||Source||None|