DS005514#

Healthy Brain Network (HBN) EEG - Release 9

Access recordings and metadata through EEGDash.

Citation: Seyed Yahya Shirazi, Alexandre Franco, Maurício Scopel Hoffmann, Nathalia B. Esper, Dung Truong, Arnaud Delorme, Michael Milham, Scott Makeig (2024). Healthy Brain Network (HBN) EEG - Release 9. 10.18112/openneuro.ds005514.v1.0.1

Modality: eeg Subjects: 295 Recordings: 11565 License: CC-BY-SA 4.0 Source: openneuro Citations: 1.0

Metadata: Complete (100%)

Quickstart#

Get Started

Install

pip install eegdash

Access the data

from eegdash.dataset import DS005514

dataset = DS005514(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 = DS005514(cache_dir="./data", subject="01")

Advanced query

dataset = DS005514(
    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{ds005514,
  title = {Healthy Brain Network (HBN) EEG - Release 9},
  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},
}

About This Dataset#

The HBN-EEG Dataset

This is Release 9 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).

Data Description

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.

View full README

The HBN-EEG Dataset

This is Release 9 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).

Data Description

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.

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.

Dataset Information#

Dataset ID	`DS005514`
Title	Healthy Brain Network (HBN) EEG - Release 9
Year	2024
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	doi:10.18112/openneuro.ds005514.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: 295
Recordings: 11565
Tasks: 10

Channels & sampling rate

Channels: 129
Sampling rate (Hz): 500.0
Duration (hours): 0.0

API Reference#

Use the DS005514 class to access this dataset programmatically.

class eegdash.dataset.DS005514(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#

Bases: EEGDashDataset

OpenNeuro dataset ds005514. Modality: eeg; Experiment type: Clinical/Intervention; Subject type: Development. Subjects: 295; recordings: 2885; 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/ds005514 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds005514

Examples

>>> from eegdash.dataset import DS005514
>>> dataset = DS005514(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