EEGDash/ds004944

Dataset of BCI2000-compatible intraoperative ECoG with neuromorphic encoding

Dataset ID: ds004944

Costa2024

Canonical aliases: BCI2000_intraop

At a glance: IEEG · Other clinical/intervention · epilepsy · 22 subjects · 44 recordings · CC0

Load this dataset

This repo is a pointer. The raw EEG data lives at its canonical source (OpenNeuro / NEMAR); EEGDash streams it on demand and returns a PyTorch / braindecode dataset.

# pip install eegdash
from eegdash import EEGDashDataset

ds = EEGDashDataset(dataset="ds004944", cache_dir="./cache")
print(len(ds), "recordings")

You can also load it by canonical alias — these are registered classes in eegdash.dataset:

from eegdash.dataset import BCI2000_intraop
ds = BCI2000_intraop(cache_dir="./cache")

If the dataset has been mirrored to the HF Hub in braindecode's Zarr layout, you can also pull it directly:

from braindecode.datasets import BaseConcatDataset
ds = BaseConcatDataset.pull_from_hub("EEGDash/ds004944")

Dataset metadata

Subjects	22
Age range	1–67 yrs, mean 19.9
Recordings	44
Tasks (count)	1
Sessions	2
Channels	3 (×10), 6 (×6), 5 (×6), 4 (×5), 23 (×3), 20 (×2), 19 (×2), 25 (×1), 22 (×1), 10 (×1), 15 (×1), 28 (×1), 27 (×1), 2 (×1), 17 (×1), 11 (×1), 21 (×1)
Sampling rate (Hz)	2000 (×44)
Total duration (h)	3.1
Size on disk	451.1 MB
Recording type	IEEG
Experimental modality	Other
Paradigm type	Clinical/Intervention
Population	Epilepsy
BIDS version	1.4.0
Source	openneuro
License	CC0
NEMAR citations	1

Tasks

• acute

Upstream README

Verbatim from the dataset's authors — the canonical description.

Overview

This dataset comprises recordings of intraoperative Electrocorticography (ECoG) from 22 patients undergoing resective epilepsy surgery. For each patient, the dataset is organized into pre-resection recording (referred to as SITUATION1A) and post-resection recording (referred to as SITUATION2A). We provide raw ECoG recordings for each patient and a derivative folder that contains all the main processing stages performed with our neuromorphic processing pipeline: https://doi.org/10.1038/s41467-024-47495-y. The pipeline preprocesses ECoG recordings in real-time and performs Asynchronous Delta Modulator (ADM) encoding with a custom BCI2000 module. The ADM-encoded data are processed by a hardware Spiking Neural Network (SNN). The SNN-encoded data are then used to detect epileptiform patterns in the ECoG. The code to perform preprocessing and ADM encoding in BCI2000, together with the code to detect epileptiform patterns from SNN-encoded data, are provided at https://github.com/CostaFilippo/BCI2000_DYNAP-SE.git. In a previous publication, this dataset has been analyzed with a offline software algorithm: https://doi.org/10.1016/j.clinph.2019.07.008. The annotations of the epileptiform patterns detected with the offline approach are provided at: sub-*/ses-SITUATION*/sub-*_ses-SITUATION*_task-acute_events.tsv. The annotations of the epileptiform patterns detected with the online neuromorphic processing are provided at derivative/sub-*/ses-SITUATION*/sub-*_ses-SITUATION*_task-EV.csv.

Dataset Structure

The derivative folder is structured as follows:

• sub-*
• ses-SITUATION1A
  • *task-BCI.dat
  • *task-ADM.csv
  • *task-SNN.csv
  • *task-EV.csv
• ses-SITUATION2A
  • *task-BCI.dat
  • *task-ADM.csv
  • *task-SNN.csv
  • *task-EV.csv

File Descriptions

• derivative
• *task-BCI.dat: BCI2000-compatible file containing the ECoG recording.
• *task-ADM.csv: ADM encoding of the ECoG recording.
• *task-SNN.csv: SNN encoding of the ECoG recording.
• *task-EV.cvs: Annotations of the detected epileptiform patterns in the ECoG recording.

Data Formats

Details of the neuromorphic processing pipeline can be found at https://doi.org/10.1038/s41467-024-47495-y.

task-BCI.dat

The BCI2000-compatible file contains the raw ECoG recording. It can be streamed in real-time using the 'FilePlayback' BCI2000 module.

task-ADM.csv

The ADM file is formatted as follows:

• pulseType: -1 for DN pulse, +1 for UP pulse.
• pulseTime: Time at which the pulse occurred.
• channel: Channel in which the pulse occurred.
• band: 0 for EEG band, 1 for HFO band

task-SNN.csv

The SNN file is formatted as follows:

• time: Time at which the SNN neuron activated.
• neuronId: Number id of the SNN neuron (DYNAP-SE numbering from 0 to 1024).
• neuronCounter: Number id of the SNN neuron (sequential numbering from 0 to 40).
• moduleName: Population (ACC_4_0; ACC_0_4), band (EEG; HFO) and module number (ch 0-7) of the neuron.
  • ACC_4_0 = ACC UP
  • ACC_0_4 = ACC DN
• moduleId: Module number (0-7).
• channelId: Channel for which the SNN neuron activated.

task-EV.csv

The EV file contains annotations of the detected epileptiform patterns with the following format:

• time: time of the detected event.
• channelId: channel id of the detected event.
• location: channel name of the detected event.

Contact Information

For inquiries or additional information, please contact Filippo.Costa@usz.ch or Johannes.Sarnthein@usz.ch

Acknowledgements

We thank V. Dimakopoulos for help in reformatting the data to BIDS. We acknowledge a grant awarded by the Swiss National Science Foundation (funded by the SNSF 204651 to JS and GI with GR and NK as project partners). The funder had no role in the design or analysis of the study.

People

Authors

• Filippo Costa
• Niklaus Krayenbühl
• Georgia Ramantani
• Ece Boran
• Kristina König
• Johannes Sarnthein (senior)

Contact

• Filippo Costa
• Johannes Sarnthein

Funding

• Swiss National Science Foundation, SNSF 204651

Links

• DOI: 10.18112/openneuro.ds004944.v1.1.0
• OpenNeuro: ds004944
• Browse 700+ datasets: EEGDash catalog
• Docs: https://eegdash.org
• Code: https://github.com/eegdash/EEGDash

Provenance

• Backend: s3 — s3://openneuro.org/ds004944
• Exact size: 473,061,643 bytes (451.1 MB)
• Ingested: 2026-04-06
• Stats computed: 2026-04-04

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Auto-generated from dataset_summary.csv and the EEGDash API. Do not edit this file by hand — update the upstream source and re-run scripts/push_metadata_stubs.py.