DS006392#

HED schema library for SCORE annotations example

Access recordings and metadata through EEGDash.

Citation: Tal Pal Attia, Kay Robbins, Dora Hermes (2025). HED schema library for SCORE annotations example. 10.18112/openneuro.ds006392.v1.0.1

Modality: ieeg Subjects: 1 Recordings: 1 License: CC0 Source: openneuro

Metadata: Good (80%)

Quickstart#

Get Started

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)

Query & Filter

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'])

Cite This Dataset

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},
}

About This Dataset#

BIDS example with HED-SCORE schema library annotations

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: 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

Dataset Information#

Dataset ID	`DS006392`
Title	HED schema library for SCORE annotations example
Year	2025
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

Found an issue with this dataset?

If you encounter any problems with this dataset (missing files, incorrect metadata, loading errors, etc.), please let us know!

Report an Issue on GitHub

Technical Details#

Subjects & recordings

Subjects: 1
Recordings: 1
Tasks: 1

Channels & sampling rate

Channels: Varies
Sampling rate (Hz): Varies
Duration (hours): 0.0

API Reference#

Use the DS006392 class to access this dataset programmatically.

class eegdash.dataset.DS006392(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#

Bases: EEGDashDataset

OpenNeuro dataset ds006392. Modality: ieeg; Experiment type: Clinical/Intervention; Subject type: Unknown. Subjects: 2; recordings: 595; 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

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, overwrite=False)[source]#

Save the dataset to disk.

Parameters:

path (str or Path) – Destination file path.
overwrite (bool, default False) – If True, overwrite existing file.

Return type:

None