EEGdash›NeMAR›ON005510

Iss. 5510 · 135 subjects · 1227 recordings · CC-BY-SA 4.0

Dataset Brief · Healthy Brain Network (HBN) EEG - Release 6

ON005510: eeg dataset, 135 subjects#

Healthy Brain Network (HBN) EEG - Release 6

Citation: Seyed Yahya Shirazi, Alexandre Franco, Maurício Scopel Hoffmann, Nathalia B. Esper, Dung Truong, Arnaud Delorme, Michael Milham, Scott Makeig (20). Healthy Brain Network (HBN) EEG - Release 6. 10.82901/nemar.on005510

135-participant EEG dataset — Healthy Brain Network (HBN) EEG - Release 6.

Data & curation Seyed Yahya Shirazi · Alexandre Franco · Maurício Scopel Hoffmann · Nathalia B. Esper · Dung Truong · Arnaud Delorme · …
Year 20 · Distributed via NeMAR
Funding See https://childmind.org/science/global-open-science/healthy-brain-network/#donors · NIH/NIMH R01MH125934 for BIDS data preparation

EEG · 129 ch500 HzBIDS 1.9.010 tasks

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#

Get Started

Install

pip install eegdash

Access the data

from eegdash.dataset import ON005510

dataset = ON005510(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 = ON005510(cache_dir="./data", subject="01")

Advanced query

dataset = ON005510(
    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{on005510,
  title = {Healthy Brain Network (HBN) EEG - Release 6},
  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 = {10.82901/nemar.on005510},
  url = {https://doi.org/10.82901/nemar.on005510},
}

§ 02Study · The README

About This Dataset#

This is Release 6 of HBN-EEG, the EEG and (soon-released) Eye-Tracking Section of the Child Mind Network Healthy Brain Network (HBN) Project, curated into the Brain Imaging Data Structure (BIDS) format. This dataset is part of a larger initiative to advance the understanding of child and adolescent mental health through collecting and analyzing neuroimaging, behavioral, and genetic data (Alexander et al., Sci Data 2017).

This dataset comprises electroencephalogram (EEG) data and behavioral responses collected during EEG experiments from >3000 participants (5-21 yo) involved in the HBN project. The data has been released in 11 separate Releases, each containing data from a different set of participants.

DOI

The HBN-EEG Dataset

Tasks

The HBN-EEG dataset includes EEG recordings from participants performing six distinct tasks, which are categorized into passive and active tasks based on the presence of user input and interaction in the experiment.

Passive Tasks

View full README

DOI

The HBN-EEG Dataset

Tasks

The HBN-EEG dataset includes EEG recordings from participants performing six distinct tasks, which are categorized into passive and active tasks based on the presence of user input and interaction in the experiment.

Passive Tasks

Resting State: Participants rested with their heads on a chin rest, following instructions to open or close their eyes and fixate on a central cross.
Surround Suppression: Participants viewed flashing peripheral disks with contrasting backgrounds, while event markers and conditions were recorded.
Movie Watching: Participants watched four short movies with different themes, with event markers recording the start and stop times of presentations.

Active Tasks

Contrast Change Detection: Participants identified flickering disks with dominant contrast changes and received feedback based on their responses.
Sequence Learning: Participants memorized and repeated sequences of flashed circles on the screen, designed for different age groups.
Symbol Search: Participants performed a computerized symbol search task, identifying target symbols from rows of search symbols.

Contents

*\*EEG Data:* High-resolution EEG recordings capture a wide range of neural activity during various tasks. *\*Behavioral Responses:* Participant responses during EEG tasks, including reaction times and accuracy. This data was originally recorded within the behavior directory of the HBN data. The data is now included with the EEG data within the events.tsv files.

Special Features

*\*Hierarchical Event Descriptors (HED):* Events, including the original EEG events and the included behavioral events, have clear explanations, including proper HED annotation suitable for systematic meta and mega analysis of the data. *\*P-Factor, Attention, Internalization and Externalization:* Derived from the CBCL questionnaire, these factors provide valuable insights into the psychopathology of the participants, adding a rich layer of interpretation to the EEG and behavioral data. *\*Data quality and availability:* We performed minimal quality control to ensure that the data was not corrupted, each task had its necessary events, and was ready for preprocessing. The results of this quality control are available in the participants.tsv file. *\*Future Releases:* We are committed to enhancing this dataset with additional, valuable features in its next stages, including:

*\*Personalized EEG Electrode Locations:* To offer more detailed insights into individual neural activity patterns. *\*Personalized Lead Field Matrix:* Enabling better understanding and interpretation of EEG data. *\*Eye-Tracking Data:* Providing a window into the visual attention and processing mechanisms during EEG experiments.

Other HBN-EEG Datasets

For access all releases of the HBN-EEG dataset, follow this link on NEMAR.org. The links to the individual releases are below:

Release 1 | DS005505

S3 URI: s3://fcp-indi/data/Projects/HBN/BIDS_EEG/cmi_bids_R1
Total subjects: 136

Release 2 | DS005506

S3 URI: s3://fcp-indi/data/Projects/HBN/BIDS_EEG/cmi_bids_R2
Total subjects: 152

Release 3 | DS005507

S3 URI: s3://fcp-indi/data/Projects/HBN/BIDS_EEG/cmi_bids_R3
Total subjects: 183

Release 4 | DS005508

S3 URI: s3://fcp-indi/data/Projects/HBN/BIDS_EEG/cmi_bids_R4
Total subjects: 324

Release 5 | DS005509

S3 URI: s3://fcp-indi/data/Projects/HBN/BIDS_EEG/cmi_bids_R5
Total subjects: 330

Release 6 | DS05510

S3 URI: s3://fcp-indi/data/Projects/HBN/BIDS_EEG/cmi_bids_R6
Total subjects: 134

Release 7 | DS005511

S3 URI: s3://fcp-indi/data/Projects/HBN/BIDS_EEG/cmi_bids_R7
Total subjects: 381

Release 8 | DS005512

S3 URI: s3://fcp-indi/data/Projects/HBN/BIDS_EEG/cmi_bids_R8
Total subjects: 257

Release 9 | DS005514

S3 URI: s3://fcp-indi/data/Projects/HBN/BIDS_EEG/cmi_bids_R9
Total subjects: 295

Release 10 | DS005515

S3 URI: s3://fcp-indi/data/Projects/HBN/BIDS_EEG/cmi_bids_R10
Total subjects: 533

Release 11 | DS005516

S3 URI: s3://fcp-indi/data/Projects/HBN/BIDS_EEG/cmi_bids_R11
Total subjects: 430

Release NC | –NOT FOR COMMERCIAL USE– This dataset is intended for research purposes only under the CC-BY-NC-SA-4.0 License and is not currently hosted on OpenNeuro/NEMAR. Any commercial use is prohibited.

S3 URI: s3://fcp-indi/data/Projects/HBN/BIDS_EEG/cmi_bids_NC
Total subjects: 458

Copyright and License

The HBN-EEG dataset is licensed under the Creative Commons Attribution 4.0 International License (CC BY SA 4.0), except for the Not-for-Commercial-Use dataset. Please cite the dataset paper (https://doi.org/10.1101/2024.10.03.615261) as well as the original HBN publication (https://dx.doi.org/10.1038/sdata.2017.181).

Acknowledgments

We would like to express our gratitude to all participants and their families, whose contributions have made this project possible. We also thank our dedicated team of researchers and clinicians for their efforts in collecting, processing, and curating this data.

§ 03Cohort · Participants

Cohort#

Dataset Statistics#

Age distribution by gender (n=135, range 5–21 yr, mean 10.2 yr)

5101520

Female · 52Male · 83

Sex composition

Female
52
Male
83
F : M ratio
0.63 : 1

39% female · n = 135 subjects with reported sex.

Channel counts: 129 ch (n=1227 recordings)

Sampling frequencies: 500.0 Hz (n=1227 recordings)

Total recording duration: 103 h

§ 04Signal · Electrodes & trace

Signal · Electrodes & live trace#

Fig. 01 Signal & montage 129 ch · EEG · 500 Hz · 135 subjects, 1227 recordings

Live trace viewer — sub-NDARAB055BPR · task-DespicableMe

Showing one representative recording out of 135 subjects and 1227 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 · 129 sensors — 129 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.

HED event descriptors word cloud

§ 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…

§ 06API · Programmatic access

API Reference#

Signature

eegdash.dataset

class

eegdash.dataset.ON005510(cache_dir, query=None, s3_bucket=None, **kwargs)

Bases: EEGDashDataset

Author (year)—

Canonical—

Importable asON005510

Sourceeegdash/dataset/registry.py · [source ↗]

class eegdash.dataset.ON005510(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#

Healthy Brain Network (HBN) EEG - Release 6

Study:: on005510 (NeMAR)
Author (year):: nan
Canonical:: —

Also importable as: ON005510, nan.

Modality: eeg. Subjects: 135; recordings: 1227; 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/on005510 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=on005510 DOI: https://doi.org/10.82901/nemar.on005510

Examples

>>> from eegdash.dataset import ON005510
>>> dataset = ON005510(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 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 — ON005510.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 on005510 to reproduce the tutorial on this dataset.

Citation

Seyed Yahya Shirazi, Alexandre Franco, Maurício Scopel Hoffmann, Nathalia B. Esper, Dung Truong, … (20). Healthy Brain Network (HBN) EEG - Release 6. 10.82901/nemar.on005510

Provenance

¹Contributed to nemar in BIDS format.

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

³Persistent identifier: 10.82901/nemar.on005510.

Related & sibling datasets

ON005505EEG · 136 subj ON005506EEG · 150 subj ON004362EEG · 109 subj ON005507EEG · 184 subj ON005628EEG · 102 subj

+ 1 more — see See Also below →

BIDS

BIDS 1.9.0

Sidecars

events · channels · eeg.json

Provenance

CC-BY-SA 4.0 · 10.82901/nemar.on005510

Machine-readable

schema.org/Dataset · Croissant

Mirrors

OpenNeuro · NEMAR · HF org

Dataset ID	`ON005510`
Title	Healthy Brain Network (HBN) EEG - Release 6
Author (year)	—
Canonical	—
Importable as	`ON005510`
Year	20
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	10.82901/nemar.on005510
Source links	OpenNeuro \| NeMAR \| Source URL

ON005510: eeg dataset, 135 subjects#

Quickstart#

About This Dataset#

Cohort#

Dataset Statistics#

Signal · Electrodes & live trace#

NEMAR Processing Statistics#

Manifest#

File Explorer#

API Reference#

Citation

Provenance

Related & sibling datasets

See Also#