Dataset of intraoperative ECoG recorded from epilepsy patients pre- and post-resection and fast ripple (FR) markings
Summary
We present an electrophysiological dataset recorded from twenty-two subjects during resective epilepsy surgery. We used standard electrodes with 10 mm inter-contact spacing (standard ECoG) in 14 surgeries and high-density grid electrodes with 5 mm spacing (hd-ECoG) in 8 surgeries. We recorded ECoG pre- and post-resection. We detected fast ripples (FR) using a previously validated automatic detector. For each recording, we provided the recorded data and FR markings. We also provide the approximate locations of recordings and resected area. The data was used in our publication doi.org/10.1016/j.clinph.2019.07.008.
Downloading the data
Using gin
Create an account on gin and download the gin client as described here. On your computer, log in using
gin login
Clone the repository using:
gin get USZ_NCH/Intraoperative_ECoG_HFO
Large data files will not be downloaded automatically. To get them, use
gin get-content <filename>
Downloaded large files will be locked (read-only). You must unlock the files using
gin unlock <filename>
To remove the contents of a large file again, use
gin lock <filename>
gin remove-content <filename>
See here for detailed information on how to use gin.
Using git annex
Make sure git and git-annex are installed on your computer. Create an account on gin and upload your public SSH key to your gin profile. Then clone the repository using
git clone git@gin.g-node.org:/USZ_NCH/Intraoperative_ECoG_HFO .git
Large data files will not be downloaded automatically. To get them, use
git annex get <filename>
Downloaded large files will be locked (read-only). You must unlock the files using
git annex unlock <filename>
To remove the contents of a large file again, use
git annex --force lock <filename>
git annex drop <filename>
See the git annex documentation for details.
Using the web browser
xxDownload the latest release as a zip file by clicking on Releases on the main page at https://gin.g-node.org/USZ_NCH/Intraoperative_ECoG_HFO . This zip file will contain all small (text) files only, while large data files will not be downloaded automatically and an empty placeholder will be put in their place. To get the full content of such a large file , download these files individually as needed from the web interface by clicking on them in the repository browser.
Repository structure
Directory datasets
xx
Directory datasets_matlab
xx
Directory code
Contains example code to help in loading and analyzing the data. The file xxexamply.py is a Python script that acts as a tutorial for loading and plotting data. The scripts xx reproduce the plots of the data found in the publication. The filesneo_utils.pyandodml_utils.pycontain useful utility routines to work with data and metadata. Finally, the fileexample.m` contains a rudimentary MATLAB script demonstrating how to use the data provided in the .hd5 files.
To run the Python example code, download the release of this repository, and install the requirements in code/requirements.txtxx. Then, run the example via
cd code
python example.pyxx
The script produces a figure saved in three different graphics file formats.
Directory code/xx
xx
Further subdirectories of code
The subdirectories python-neo, python-odml, and elephant contain snapshots of the Neo[1], odML[2], and Elephant[3] libraries, respectively, that are required by the example scripts and the xx loading routine. odML provides an API to handle the metadata files.
Updates
Updated versions of the codes will be provided at:
https://gin.g-node.org/USZ_NCH/Intraoperative_ECoG_HFO
This includes, in particular, xx
Related Publications
- Boran Ece, Ramantani G, Krayenbühl N, Schreiber M, König K, Fedele T, Sarnthein J. High-density ECoG improves the detection of high frequency oscillations that predict seizure outcome. Clinph. doi.org/10.1016/j.clinph.2019.07.008
- Prediction of seizure outcome improved by fast ripples detected in low-noise intraoperative corticogram.
- Automatic detection of high frequency oscillations during epilepsy surgery predicts seizure outcome.
Licensing
Dataset of simultaneous scalp EEG and intracranial EEG recordings and human medial temporal lobe units during a verbal working memory task in the directories xxdatasets and datasets_matlab by xxx is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
xxxAll code in the directories code, code/python-odml, are each published under the BSD 3 clause licenses. See the LICENSE.txt or LICENSE files in the corresponding directories for the full license.
Ece Boran