https://github.com/sccn/nsgportal_manuscriptsupport
Scripts and data supporting examples in the manuscript: Bringing High-Performance Computing into EEGLAB: The Open EEGLAB Portal
https://github.com/sccn/nsgportal_manuscriptsupport
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Scripts and data supporting examples in the manuscript: Bringing High-Performance Computing into EEGLAB: The Open EEGLAB Portal
- Host: GitHub
- URL: https://github.com/sccn/nsgportal_manuscriptsupport
- Owner: sccn
- Created: 2019-08-13T14:17:30.000Z (almost 7 years ago)
- Default Branch: master
- Last Pushed: 2019-12-03T07:04:06.000Z (over 6 years ago)
- Last Synced: 2025-01-05T06:43:13.368Z (over 1 year ago)
- Language: MATLAB
- Size: 24.1 MB
- Stars: 0
- Watchers: 5
- Forks: 0
- Open Issues: 0
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Metadata Files:
- Readme: README.md
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README
## Supporting files for manuscript: Bringing High-Performance Computing into EEGLAB: The Open EEGLAB Portal
Supporting files (not data) for the manuscript:
**Bringing High-Performance Computing into EEGLAB: The Open EEGLAB Portal** by Ramon Martinez-Cancino, Dung Truong, Fiorenzo Artoni, Kenneth Kreutz-Delgado, Amitava Majumdar, Subhashini Sivagnanam, Kenneth Yoshimoto, Scott Makeig and Arnaud Delorme.(In preparation)
## Content
1. Folder *oep_runica*: Sample job used in the manuscript. It contains the script and data to run in NSG.
2. Folder *oep_runica_plugin*: Sammple plug-in featuring *nsgportal* command-line tools
3. Folder *relica_local_test*: Script for testing RELICA (NSG-capable) performance. Saves the time and computer characteristics where the test is performed.
4. Folder *wh_data* with the scripts for:
1. Import the EEG data from the raw (.fif) files (*wh_extracteegsubj11.m*)
2. Preprocessing and saving the EEG data in its final format for the job test (*wh_preprocessing_subj11.m*).
3. Running data import and processing scripts (*wh_subj11_runall.m*). This is the one that should be run by the users.
The data used here correspond to the first run from subject 11 in the dataset published by:
Henson, R.N., Wakeman, D.G., Litvak, V. & Friston, K.J. (2011).
A Parametric Empirical Bayesian framework for the EEG/MEG inverse
problem: generative models for multisubject and multimodal integration.
Frontiers in Human Neuroscience, 5, 76, 1-16.
The data was obtained from the OpenNeuro project (https://www.openneuro.org). Accession #: ds000117.