EEGdash›OpenNeuro›DS003555
Iss. 3555 · 30 subjects · 30 recordings · CC0
Dataset Brief · Dataset of EEG recordings of pediatric patients with epilepsy…

DS003555: eeg dataset, 30 subjects#

Dataset of EEG recordings of pediatric patients with epilepsy based on the 10-20 system

Citation: Dorottya Cserpan, Ece Boran, Richard Rosch, San Pietro Lo Biundo, Georgia Ramantani, Johannes Sarnthein (—). Dataset of EEG recordings of pediatric patients with epilepsy based on the 10-20 system. 10.18112/openneuro.ds003555.v1.0.1

30-participant EEG dataset — Dataset of EEG recordings of pediatric patients with epilepsy based on the 10-20 system.

Data & curation Dorottya Cserpan · Ece Boran · Richard Rosch · San Pietro Lo Biundo · Georgia Ramantani · Johannes Sarnthein
Funding Swiss National Science Foundation (CRSK-3_190895 to G.R. and J.S.).
EEG · 23 (27), 24 (3) ch1024 HzBIDS 1.4.0Task · hfoEpilepsyResting StateClinical/Intervention
Layer 01Study
What was asked
Hypothesis, independent & dependent variables, paradigm, cohort, and the editorial caveats around what the recordings can and cannot answer.
Layer 02Signal · BIDS
What was recorded
Sidecars, channels & electrodes, coordinate system, event semantics, and quality stats from the NEMAR pipeline when available.
Layer 03Training · ML
What you can train on
Recommended access modes — MNE Raw, braindecode windows, PyTorch DataLoader — plus the targets the metadata makes addressable.
§ 01Access · Get started

Quickstart#

Install

pip install eegdash

Access the data

from eegdash.dataset import DS003555

dataset = DS003555(cache_dir="./data")
# Get the raw object of the first recording
raw = dataset.datasets[0].raw
print(raw.info)

Filter by subject

dataset = DS003555(cache_dir="./data", subject="01")

Advanced query

dataset = DS003555(
    cache_dir="./data",
    query={"subject": {"$in": ["01", "02"]}},
)

Iterate recordings

for rec in dataset:
    print(rec.subject, rec.raw.info['sfreq'])

If you use this dataset in your research, please cite the original authors.

BibTeX

@dataset{ds003555,
  title = {Dataset of EEG recordings of pediatric patients with epilepsy based on the 10-20 system},
  author = {Dorottya Cserpan and Ece Boran and Richard Rosch and San Pietro Lo Biundo and Georgia Ramantani and Johannes Sarnthein},
  doi = {10.18112/openneuro.ds003555.v1.0.1},
  url = {https://doi.org/10.18112/openneuro.ds003555.v1.0.1},
}
§ 02Study · The README

About This Dataset#

High-frequency oscillations in scalp EEG are promising non-invasive biomarkers of epileptogenicity. However, it is unclear how high-frequency oscillations are impacted by age in the pediatric population.

We recorded and processed the first 3 hours of sleep EEG data in 30 children and adolescents with focal or generalized epilepsy. We used an automated and clinically validated high-frequency oscillation detector to determine ripple rates (80-250 Hz) in bipolar channels. The software for the detection of HFOs is freely available at the GitHub repository (ZurichNCH/Automatic-High-Frequency-Oscillation-Detector). Furthermore HFO markings are also added in this database for the selected N3 intervals.

Dataset of EEG recordings containing HFO markings for 30 pediatric patients with epilepsy

Summary

Repository structure

Main directory (hfo/)

View full README

Dataset of EEG recordings containing HFO markings for 30 pediatric patients with epilepsy

Summary

Repository structure

Main directory (hfo/)

Contains metadata files in the BIDS standard about the participants and the study. Folders are explained below.

Subfolders

*hfo/sub-**/ Contains folders for each subject, named sub-<subject number> and session information. *hfo/sub-**/ses-01/eeg Contains the raw eeg data in .edf format for each subject. The duration is typically 3 hours, that was recorded in the beginning of the sleep. Details about the channels are given in the corresponding .tsv file. * hfo/derivatives

Besides containingsubfolders for the raw data, there are two .json files. The events_description.json explains the meaning of the columns of the event description tsv files (in the subfolders).

The interval_description.json explains the meaning of the columns of the interval description tsv files (in the subfolders). *hfo/derivatives/sub-**/ses-01/eeg/ Contains processed data for each subject. Based on the sleep annotations, first we identified the sleep stages. Then we cut 5 minutes data intervals from the N3 sleep stages. We applied bipolar referencing by considering all nearest neighbour chanels, thus resulting in 52 bipolar channels. Each run corresponds to one 5 minute data interval. The DataIntervals.tsv file provides information about how the various runs are related to the raw data by providing the start and end indeces. Besides the .edf and channel descriptor .tsv files there is an other .tsv file containing the detected candidate event details. Eg. sub-26_ses-01_task-hfo_run-01_events.tsv contains for subject 26 for the first processed data interval the event markings as indeces with additional features of this event described in the abovementioned events_description.json file.

Related materials

The code for HFO detection is available at ZurichNCH/Automatic-High-Frequency-Oscillation-Detector

Support

For questions on the dataset or the task, contact Johannes Sarnthein at johannes.sarnthein@usz.ch.

§ 03Cohort · Participants

Cohort#

Dataset Statistics#

Age distribution by gender (n=30, range 1–17 yr, mean 7.5 yr)

051015
Female · 16Male · 14

Sex composition

30
subjects
Female
16
Male
14
F : M ratio
1.14 : 1
53% female · n = 30 subjects with reported sex.

Channel counts (ch)

2324

Sampling frequencies: 1024.0 Hz (n=30 recordings)

§ 04Signal · Electrodes & trace

Signal · Electrodes & live trace#

Fig. 01 Signal & montage 23 (27), 24 (3) ch · EEG · 1024 Hz · 30 subjects, 30 recordings
Live trace viewer — sub-13 · ses-01 · task-hfo

Showing one representative recording out of 30 subjects and 30 recordings in this dataset. Browse the full set on OpenNeuro; drop any other _eeg.{set,edf,bdf,vhdr} file onto the viewer (or pass ?eeg=<url>) to inspect it.

Electrode layout — EEG · 23 sensors — 23 channels

NEMAR Processing Statistics#

The plots below are generated by NEMAR’s automated EEG pipeline. The histogram shows pipeline success for data cleaning and ICA decomposition, the percentage of data frames and EEG channels retained after artefact removal, line noise per channel (RMS, dB), and the age/gender distribution of participants.

NEMAR pipeline statistics — DS003555
HED event descriptors word cloud HED event descriptors word cloud — DS003555
§ 05Manifest · BIDS tree

Manifest#

File Explorer#

Browse the BIDS file structure of this dataset. Records are fetched on demand from the EEGDash catalog the first time you open the explorer.

Recordings—
Files—
Subjects—
Modalities—
Click to load file structure…
Full dataset metadata table

Dataset ID

DS003555

Title

Dataset of EEG recordings of pediatric patients with epilepsy based on the 10-20 system

Author (year)

Cserpan2021

Canonical

—

Importable as

DS003555, Cserpan2021

Year

—

Authors

Dorottya Cserpan, Ece Boran, Richard Rosch, San Pietro Lo Biundo, Georgia Ramantani, Johannes Sarnthein

License

CC0

Citation / DOI

10.18112/openneuro.ds003555.v1.0.1

Source links

OpenNeuro | NeMAR | Source URL
Copy-paste BibTeX
@dataset{ds003555,
  title = {Dataset of EEG recordings of pediatric patients with epilepsy based on the 10-20 system},
  author = {Dorottya Cserpan and Ece Boran and Richard Rosch and San Pietro Lo Biundo and Georgia Ramantani and Johannes Sarnthein},
  doi = {10.18112/openneuro.ds003555.v1.0.1},
  url = {https://doi.org/10.18112/openneuro.ds003555.v1.0.1},
}
§ 06API · Programmatic access

API Reference#

Signature
eegdash.dataset
class
eegdash.dataset.DS003555(cache_dir, query=None, s3_bucket=None, **kwargs)
Bases: EEGDashDataset
Author (year)Cserpan2021
Canonical—
Importable asDS003555 · Cserpan2021
Sourceeegdash/dataset/registry.py · [source ↗]
class eegdash.dataset.DS003555(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#

Dataset of EEG recordings of pediatric patients with epilepsy based on the 10-20 system

Study:

ds003555 (OpenNeuro)

Author (year):

Cserpan2021

Canonical:

—

Also importable as: DS003555, Cserpan2021.

Modality: eeg. Subjects: 30; recordings: 30; tasks: 1.

Parameters:

  • cache_dir (str | Path) – Directory where data are cached locally.

  • query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.

  • s3_bucket (str | None) – Base S3 bucket used to locate the data.

  • **kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir#

Local dataset cache directory (cache_dir / dataset_id).

Type:

Path

query#

Merged query with the dataset filter applied.

Type:

dict

records#

Metadata records used to build the dataset, if pre-fetched.

Type:

list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/ds003555 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds003555 DOI: https://doi.org/10.18112/openneuro.ds003555.v1.0.1 NEMAR citation count: 8

Examples

>>> from eegdash.dataset import DS003555
>>> dataset = DS003555(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()
__init__(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#
save(path: str, overwrite: bool = False, offset: int = 0)[source]#

Save datasets to files by creating one subdirectory for each dataset:

path/
    0/
        0-raw.fif | 0-epo.fif
        description.json
        raw_preproc_kwargs.json (if raws were preprocessed)
        window_kwargs.json (if this is a windowed dataset)
        window_preproc_kwargs.json  (if windows were preprocessed)
        target_name.json (if target_name is not None and dataset is raw)
    1/
        1-raw.fif | 1-epo.fif
        description.json
        raw_preproc_kwargs.json (if raws were preprocessed)
        window_kwargs.json (if this is a windowed dataset)
        window_preproc_kwargs.json  (if windows were preprocessed)
        target_name.json (if target_name is not None and dataset is raw)
Parameters:

  • path (str) –

    Directory in which subdirectories are created to store

    -raw.fif | -epo.fif and .json files to.

  • overwrite (bool) – Whether to delete old subdirectories that will be saved to in this call.

  • offset (int) – If provided, the integer is added to the id of the dataset in the concat. This is useful in the setting of very large datasets, where one dataset has to be processed and saved at a time to account for its original position.

Access modesMNE → braindecode → PyTorch → ML
.rawMNE Raw object — standard tools (filter, epoch, ICA, plot_psd).mne
BaseConcatDatasetEach record is a lazy BaseDataset from braindecode — windowed via create_windows_from_events.braindecode
DataLoaderWraps the windowed dataset into a PyTorch DataLoader; supports parallel workers and on-the-fly augmentations.pytorch
Zarr cacheOptional braindecode Zarr mirror for fast resume; persisted to cache_dir.zarr
Hugging FacePre-bundled mirror at EEGDash/ds003555 · pull with datasets.load_dataset("EEGDash/ds003555").huggingface
Croissant 1.0Machine-readable JSON-LD descriptor — DS003555.croissant.json (MLCommons schema, ingestible by PyTorch / TensorFlow / JAX).mlcommons
Examples using EEGDashcurated · start here
Find datasets with the EEGDash APIQuery the catalogue, filter by task or modality, list candidates.Load one EEG recordingResolve a single record to an MNE Raw with channels and events.EEG recording to PyTorch DataLoaderWrap braindecode windows in a DataLoader for model training.Preprocess EEG and create windowsFilter, resample, epoch — and persist the windowed dataset.Save and reload prepared dataCache a windowed dataset to disk and reattach it without recompute.Download a dataset locallyPrefetch BIDS files to a local cache and validate the layout.

Swap any load_dataset(...) call for ds003555 to reproduce the tutorial on this dataset.

Citation

Dorottya Cserpan, Ece Boran, Richard Rosch, San Pietro Lo Biundo, Georgia Ramantani, … (n.d.). Dataset of EEG recordings of pediatric patients with epilepsy based on the 10-20 system. 10.18112/openneuro.ds003555.v1.0.1

Provenance

¹Contributed to openneuro in BIDS format.

²Curated & ingested by the EEGDash catalog; see CITATION.cff for canonical reference.

³Persistent identifier: 10.18112/openneuro.ds003555.v1.0.1.

BIDS
BIDS 1.4.0
Sidecars
channels
Machine-readable
schema.org/Dataset · Croissant

See Also#