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@@ -66,36 +66,9 @@ See the *git annex* documentation for details.
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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.
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-## Repository structure
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-### Directory datasets
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-### Directory datasets_matlab
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-### Directory code
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-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 files `neo_utils.py` and `odml_utils.py` contain useful utility routines to work with data and metadata. Finally, the file `example.m` contains a rudimentary MATLAB script demonstrating how to use the data provided in the .hd5 files.
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-To run the Python example code, download the release of this repository, and install the requirements in `code/requirements.txt`xx. Then, run the example via
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-```
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- cd code
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- python example.pyxx
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-```
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-The script produces a figure saved in three different graphics file formats.
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-### Directory code/xx
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-xx
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-### Further subdirectories of code
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-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.
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-* [1] https://github.com/G-Node/python-odml
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## Updates
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Updated versions of the codes will be provided at:
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https://gin.g-node.org/USZ_NCH/Intraoperative_ECoG_HFO
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-This includes, in particular, xx
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## Related Publications
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* 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
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Ece Boran