EEGdash›OpenNeuro›DS006392
Iss. 6392 · 1 subjects · 1 recordings · CC0
Dataset Brief · HED schema library for SCORE annotations example

DS006392: ieeg dataset, 1 subjects#

HED schema library for SCORE annotations example

Citation: Tal Pal Attia, Kay Robbins, Dora Hermes (20). HED schema library for SCORE annotations example. 10.18112/openneuro.ds006392.v1.0.1

1-participant iEEG dataset — HED schema library for SCORE annotations example.

Data & curation Tal Pal Attia · Kay Robbins · Dora Hermes
Year 20 · Distributed via OpenNeuro
Funding National Institute of Health Grant R01MH126700 (SM) · National Institute of Health Grant R01MH122258 (DH)
iEEG · 166 ch512 HzBIDS 1.9.0Task · photicstimVisualPerception
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 DS006392

dataset = DS006392(cache_dir="./data")
# Get the raw object of the first recording
raw = dataset.datasets[0].raw
print(raw.info)

Filter by subject

dataset = DS006392(cache_dir="./data", subject="01")

Advanced query

dataset = DS006392(
    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{ds006392,
  title = {HED schema library for SCORE annotations example},
  author = {Tal Pal Attia and Kay Robbins and Dora Hermes},
  doi = {10.18112/openneuro.ds006392.v1.0.1},
  url = {https://doi.org/10.18112/openneuro.ds006392.v1.0.1},
}
§ 02Study · The README

About This Dataset#

The HED schema library for the Standardized Computer-based Organized Reporting of EEG (SCORE) can be used to add annotations for BIDS datasets. The annotations are machine readable and validated with the BIDS and HED validators.

This example is related to the following preprint:

BIDS example with HED-SCORE schema library annotations

Dora Hermes, Tal Pal Attia, Sándor Beniczky, Jorge Bosch-Bayard, Arnaud Delorme, Brian Nils Lundstrom, Christine Rogers, Stefan Rampp, Seyed Yahya Shirazi, Dung Truong, Pedro Valdes-Sosa, Greg Worrell, Scott Makeig, Kay Robbins. Hierarchical Event Descriptor library schema for EEG data annotation. arXiv preprint arXiv:2310.15173. 2024 Oct 27.

General information

This BIDS example dataset includes iEEG data from one subject that were measured during clinical photic stimulation. Intracranial EEG data were collected at Mayo Clinic Rochester, MN under IRB#: 15-006530.

Events

The events are annotated according to the HED-SCORE schema library. Data are annotated by adding a column for annotations in the _events.tsv. The levels and annotations in this column are defined in the _events.json sidecar as HED tags.

More information

HED: https://www.hedtags.org/ HED schema library for SCORE: hed-standard/hed-schema-library

Contact

Dora Hermes: hermes.dora@mayo.edu

§ 03Cohort · Participants

Cohort#

Dataset Statistics#

Channel counts: 166 ch (n=1 recordings)

Sampling frequencies: 512.0 Hz (n=1 recordings)

Total recording duration: 3 min

§ 04Signal · Electrodes & trace

Signal · Electrodes & live trace#

Fig. 01 Signal & montage 166 ch · iEEG · 512 Hz · 1 subjects, 1 recordings
Electrode layout — iEEG · 166 sensors — 166 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 — DS006392
HED event descriptors word cloud HED event descriptors word cloud — DS006392
§ 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

DS006392

Title

HED schema library for SCORE annotations example

Author (year)

Attia2025

Canonical

—

Importable as

DS006392, Attia2025

Year

20

Authors

Tal Pal Attia, Kay Robbins, Dora Hermes

License

CC0

Citation / DOI

doi:10.18112/openneuro.ds006392.v1.0.1

Source links

OpenNeuro | NeMAR | Source URL
Copy-paste BibTeX
@dataset{ds006392,
  title = {HED schema library for SCORE annotations example},
  author = {Tal Pal Attia and Kay Robbins and Dora Hermes},
  doi = {10.18112/openneuro.ds006392.v1.0.1},
  url = {https://doi.org/10.18112/openneuro.ds006392.v1.0.1},
}
§ 06API · Programmatic access

API Reference#

Signature
eegdash.dataset
class
eegdash.dataset.DS006392(cache_dir, query=None, s3_bucket=None, **kwargs)
Bases: EEGDashDataset
Author (year)Attia2025
Canonical—
Importable asDS006392 · Attia2025
Sourceeegdash/dataset/registry.py · [source ↗]
class eegdash.dataset.DS006392(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#

HED schema library for SCORE annotations example

Study:

ds006392 (OpenNeuro)

Author (year):

Attia2025

Canonical:

—

Also importable as: DS006392, Attia2025.

Modality: ieeg; Experiment type: Perception; Subject type: Unknown. Subjects: 1; recordings: 1; 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/ds006392 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds006392 DOI: https://doi.org/10.18112/openneuro.ds006392.v1.0.1

Examples

>>> from eegdash.dataset import DS006392
>>> dataset = DS006392(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/ds006392 · pull with datasets.load_dataset("EEGDash/ds006392").huggingface
Croissant 1.0Machine-readable JSON-LD descriptor — DS006392.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 ds006392 to reproduce the tutorial on this dataset.

Citation

Tal Pal Attia, Kay Robbins, Dora Hermes (20). HED schema library for SCORE annotations example. 10.18112/openneuro.ds006392.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.ds006392.v1.0.1.

BIDS
BIDS 1.9.0
Sidecars
not yet probed
Machine-readable
schema.org/Dataset · Croissant

See Also#