Intraoperative_ECoG_HFO/README.md

Dataset of intraoperative pre- and post-resection ECoG recorded from epilepsy patients 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 and did visual validation. For each recording, we provide the recorded data and FR markings. We also provide the approximate locations of recordings and resected area. The data was used in our publication "High-density ECoG improves the detection of high frequency oscillations that predict seizure outcome" (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

Download 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

Directory datasets_matlab

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.

• [1] https://github.com/G-Node/python-odml

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

This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License. See LICENSE.txt for the full license.