eegdash.dataset.EEG2025R9MINI#

Healthy Brain Network (HBN) EEG - Release 9 (BDF Converted) (OpenNeuro eeg2025r9mini). Access recordings and metadata through EEGDash.

Modality: [‘eeg’] Tasks: 0 License: CC-BY-SA 4.0 Subjects: 0 Recordings: 0 Source: nemar

Dataset Information#

Dataset ID

EEG2025R9MINI

Title

Healthy Brain Network (HBN) EEG - Release 9 (BDF Converted)

Year

Unknown

Authors

Seyed Yahya Shirazi, Alexandre Franco, Maurício Scopel Hoffmann, Nathalia B. Esper, Dung Truong, Arnaud Delorme, Michael Milham, Scott Makeig

License

CC-BY-SA 4.0

Citation / DOI

10.18112/openneuro.ds005514.v1.0.1

Source links

OpenNeuro | NeMAR | Source URL
Copy-paste BibTeX
@dataset{eeg2025r9mini,
  title = {Healthy Brain Network (HBN) EEG - Release 9 (BDF Converted)},
  author = {Seyed Yahya Shirazi and Alexandre Franco and Maurício Scopel Hoffmann and Nathalia B. Esper and Dung Truong and Arnaud Delorme and Michael Milham and Scott Makeig},
  doi = {10.18112/openneuro.ds005514.v1.0.1},
  url = {https://doi.org/10.18112/openneuro.ds005514.v1.0.1},
}

Highlights#

Subjects & recordings

  • Subjects: 0

  • Recordings: 0

  • Tasks: 0

Channels & sampling rate

  • Channels: 129

  • Sampling rate (Hz): 100.0

  • Duration (hours): 0

Tasks & conditions

  • Tasks: 0

  • Experiment type: Unknown

  • Subject type: Unknown

Files & format

  • Size on disk: Unknown

  • File count: Unknown

  • Format: Unknown

License & citation

  • License: CC-BY-SA 4.0

  • DOI: 10.18112/openneuro.ds005514.v1.0.1

Provenance

Quickstart#

Install

pip install eegdash

Load a recording

from eegdash.dataset import EEG2025R9MINI

dataset = EEG2025R9MINI(cache_dir="./data")
recording = dataset[0]
raw = recording.load()

Filter/query

dataset = EEG2025R9MINI(cache_dir="./data", subject="01")

dataset = EEG2025R9MINI(
    cache_dir="./data",
    query={"subject": {"$in": ["01", "02"]}},
)

Quality & caveats#

  • No dataset-specific caveats are listed in the available metadata.

API#

class eegdash.dataset.EEG2025R9MINI(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#

Bases: EEGDashDataset

OpenNeuro dataset EEG2025r9mini. Modality: eeg; Experiment type: Unknown; Subject type: Unknown. Subjects: 20; recordings: 237; tasks: 10.

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/EEG2025r9mini NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=EEG2025r9mini DOI: https://doi.org/10.18112/openneuro.ds005514.v1.0.1

Examples

>>> from eegdash.dataset import EEG2025R9MINI
>>> dataset = EEG2025R9MINI(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

See Also#