EEGdash›OpenNeuro›HBN_r9_bdf_mini
Iss. 9 · — subjects · — recordings · See source
Dataset Brief · HBN_r9_bdf_mini

HBN_r9_bdf_mini: EEG dataset#

Dataset from OpenNeuro.

Citation: Unknown (—). **.

A EEG dataset distributed through EEGDash with standardized BIDS metadata..

Data & curation Authors unspecified
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 HBN_r9_bdf_mini

dataset = HBN_r9_bdf_mini(cache_dir="./data")
# Get the raw object of the first recording
raw = dataset.datasets[0].raw
print(raw.info)

Filter by subject

dataset = HBN_r9_bdf_mini(cache_dir="./data", subject="01")

Advanced query

dataset = HBN_r9_bdf_mini(
    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{hbn_r9_bdf_mini,
}
§ 02Study · The README

About This Dataset#

No README content is available for this dataset.

§ 03Cohort · Participants

Cohort#

§ 04Signal · Electrodes & trace

Signal · Electrodes & live trace#

Fig. 01 Signal & montage — ch · EEG · Varies · — subjects, — recordings

No scalp electrode layout is currently indexed for this dataset. Once the eegdash montage registry ingests it, the interactive viewer will appear here automatically.

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 — HBN_R9_BDF_MINI
HED event descriptors word cloud HED event descriptors word cloud — HBN_R9_BDF_MINI
§ 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

HBN_R9_BDF_MINI

Title

—

Author (year)

—

Canonical

—

Importable as

HBN_R9_BDF_MINI

Year

—

Authors

Unknown

License

—

Citation / DOI

Unknown

Source links

OpenNeuro | NeMAR
§ 06API · Programmatic access

API Reference#

Signature
eegdash.dataset
class
eegdash.dataset.HBN_r9_bdf_mini(cache_dir, query=None, s3_bucket=None, **kwargs)
Bases: EEGDashDataset
Author (year)—
Canonical—
Importable asHBN_R9_BDF_MINI
Sourceeegdash/dataset/registry.py · [source ↗]
eegdash.dataset.HBN_r9_bdf_mini[source]#

alias of EEG2025R9MINI

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 FaceNo per-dataset mirror published yet — browse the EEGDash org listing for sibling datasets. See the datasets loader API.huggingface
Croissant 1.0Machine-readable JSON-LD descriptor — HBN_r9_bdf_mini.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 hbn_r9_bdf_mini to reproduce the tutorial on this dataset.

Citation

Authors unspecified (n.d.). HBN_r9_bdf_mini.

Provenance

¹Contributed to OpenNeuro in BIDS format.

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

Related & sibling datasets

No sibling datasets cross-linked for this modality yet.

BIDS
version not on file
Sidecars
not yet probed
Provenance
See source · DOI not on file
Machine-readable
schema.org/Dataset · Croissant
Mirrors
OpenNeuro · NEMAR · HF org

See Also#