Update README.md

README.md

@@ -7,4 +7,99 @@ tags:
 - seizure
 - epilepsy
 pretty_name: SWEC iEEG Dataset
----
+---
+
+## SWEC iEEG Dataset
+
+[![arXiv](https://img.shields.io/badge/arXiv-2506.20354-00ff00.svg)](https://arxiv.org/abs/2506.20354) [![GitHub](https://img.shields.io/badge/GitHub-%23121011.svg?logo=github&logoColor=white&label=MVPFormer)](https://github.com/IBM/multi-variate-parallel-transformer) [![Static Badge](https://img.shields.io/badge/SWEC-iEEG%20SWEC%20Dataset-fedcba?style=flat&label=Data)](https://mb-neuro.medical-blocks.ch/public_access/databases/ieeg/swec_ieeg)
+
+## Dataset summary
+
+The SWEC iEEG Dataset contains fully anonymised multi-channel iEEG recordings collected from a total of 68 subjects suffering from pharmacoresistent epilepsy undergoing pre-surgical evaluation for epilepsy. The data was recorded at the Sleep Wake Epilepsy Center (SWEC) of the Department of Neurology at the Inselspital in Bern, Switzerland. The dataset includes a total of 9328 hours of signal and 704 ictal events, annotated by board-certified epileptologist Prof. Kaspar Schindler.
+Dataset structure
+
+The dataset is divided into 68 folders, one per subject. All the files in each folder are prefixed with the ID of the corresponding patient. Each folder contains the entire data for one patient divided into multiple HDF5 **part** files, with parts being roughly 10GB in (uncompressed) size. Each folder also contains a **total** file in the HDF5 VDS format, combining all the parts into one continuous virtual recording for easy access and manipulation. The **total** file includes the ictal annotations.
+
+The dataset is comprised of 696 files across 68 folders, for a total (compressed, see Recordings details) size of 4.6TB.
+
+### Structure example
+
+As an example of a typical subject folder, here is subject `ID04`
+```
+:
+|-- ...
+|-- ID03
+|-- ID04
+| |-- ID04_part_1.h5
+| |-- ID04_part_2.h5
+| |-- ID04_total.h5
+|-- ID05
+|-- ...
+```
+
+### Recordings
+
+The recordings can be accessed at the `data/ieeg` dataset in every **part** file, or alternatively at the `data/ieeg` dataset of the **total** file for a unified view. Access through the **total** file is recommended. The `ieeg` dataset has shape `(C, T)` and is chunked into pieces containing 3 minutes of signal across all the channels (e.g., each chunk is `(64, 92160)` for a recording with sample rate 512Hz and 64 channels), to fit a suitable random access pattern. Moreover, the data is compressed with `lz4hc`, requiring an appropriate decoder for reading (e.g., [h5py](https://www.h5py.org/) with [hdf5plugin](https://github.com/silx-kit/hdf5plugin)).
+
+### Annotations
+
+The ictal annotations can be accessed at the `data/seizures` dataset of the **total** file. The seizures
+dataset is a structured array with fields `onsets` and `offsets`, representing the seizures onsets and offsets respectively in seconds since the beginning of recording.
+
+### File integrity
+
+The **total** file also contains the datasets `info/files` and `info/checksums`. The `files` dataset includes the list of parts name associated with the specific patient, while the `checksum`
+dataset includes the blake2b checksums of each **part** file to verify their integrate (e.g., with the `b2sum <part>` utility).
+
+### Other attributes
+
+Every file contains attributes `patient` with the patient ID, `channels` with the number of channels, and `sampling_rate` with the sampling rate in Hz.
+
+---
+
+## Dataset curation
+
+### Preparation
+
+The iEEG signals were recorded intracranially by strip, grid, and depth electrodes. After 16-bit analog-to-digital conversion, the recordings were visually inspected for removal of channels corrupted by artifacts. The signals were then digitally band-pass filtered between 0.5 and 150 Hz using a fourth-order Butterworth forward-backward filter and finally downsampled to either 512Hz or 1024Hz.
+
+### Ethical considerations
+
+All the subjects gave written informed consent that their iEEG data might be used for research and teaching purposes. The decision on the necessity for iEEG recordings, the electrode implantation scheme, and the decision about surgical therapy were made entirely on clinical grounds. These decisions were taken prior to and completely independently from the compilation of this dataset.
+
+---
+
+## Additional information
+
+### Dataset curators
+
+The dataset was created by Kaspar Schindler and the team at the SWEC. The dataset was further prepared for public availability by Francesco Carzaniga, Kaspar Schindler, Abbas Rahimi, and the team at the SWEC.
+
+### Initial version
+
+An initial version containing the first 18 subjects with a different format can be found at this [location](http://ieeg-swez.ethz.ch/). Please note that that version is considered obsolete and might be made unavailable without notice.
+
+### Licensing
+
+The iEEG SWEC dataset is licensed using the [Community Data License Agreement – Permissive, Version 2.0](https://cdla.dev/permissive-2-0/).
+
+### Disclaimer
+
+This dataset may only be used for research. For other applications any liability is denied. In particular, the dataset must not be used for diagnostic purposes.
+
+### Citation
+
+If you are using this dataset, please cite the following:
+
+```
+@article{carzaniga2025foundation,
+title={A foundation model with multi-variate parallel attention to generate neuronal activity},
+author={Carzaniga, Francesco and Hersche, Michael and Sebastian, Abu and Schindler, Kaspar and Rahimi, Abbas},
+journal={arXiv preprint arXiv:2506.20354},
+year={2025}
+}
+```
+
+### Contact
+
+Francesco Carzaniga ([email protected])