NM000103: eeg dataset, 447 subjects#

Healthy Brain Network EEG - Not for Commercial Use

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 (20). Healthy Brain Network EEG - Not for Commercial Use. 10.82901/nemar.nm000103

Modality: eeg Subjects: 447 Recordings: 3522 License: CC-BY-NC-SA 4.0 Source: nemar

Metadata: Complete (100%)

Quickstart#

Get Started

Install

pip install eegdash

Access the data

from eegdash.dataset import NM000103

dataset = NM000103(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 = NM000103(cache_dir="./data", subject="01")

Advanced query

dataset = NM000103(
    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{nm000103,
  title = {Healthy Brain Network EEG - Not for Commercial Use},
  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.nm000103},
  url = {https://doi.org/10.82901/nemar.nm000103},
}

About This Dataset#

Overview

This is NOT for Commercial-Use Release 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 participants involved in the HBN project.

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. This 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 behavioral questionnaires, these factors provide valuable insights into the internalizing and externalizing behaviors of participants, adding a rich layer of psychological 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.

Copyright and License

This dataset is licensed under the non-commercial version of the Creative Common Attributions version 4.0 license (CC BY NC SA 4.0) based on the participant’s consent. Subjects (or their legal gurdians) did NOT provide consent for their data to be used for any commercial pourposes.

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	`NM000103`
Title	Healthy Brain Network EEG - Not for Commercial Use
Author (year)	`Shirazi2017`
Canonical	—
Importable as	`NM000103`, `Shirazi2017`
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-NC-SA 4.0
Citation / DOI	10.82901/nemar.nm000103
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: 447
Recordings: 3522
Tasks: 10

Channels & sampling rate

Channels: 129
Sampling rate (Hz): 500.0
Duration (hours): 285.0150427777777

Electrode Layout#

Electrode layout — EEG · 129 sensors — 129 channels

Dataset Statistics#

Age distribution (n=447, range 5–21 yr)

5101520

Sex distribution

189

258

Female Male Total: 447

Channel counts: 129 ch (n=3522 recordings)

Sampling frequencies: 500.0 Hz (n=3522 recordings)

Total recording duration: 285 h

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

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.

Files: —

Size: —

Subjects: —

Click to load file structure…

API Reference#

Use the NM000103 class to access this dataset programmatically.

class eegdash.dataset.NM000103(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#

Bases: EEGDashDataset

Healthy Brain Network EEG - Not for Commercial Use

Study:: nm000103 (NeMAR)
Author (year):: Shirazi2017
Canonical:: —

Also importable as: NM000103, Shirazi2017.

Modality: eeg. Subjects: 447; recordings: 3522; 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/nm000103 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=nm000103 DOI: https://doi.org/10.82901/nemar.nm000103

Examples

>>> from eegdash.dataset import NM000103
>>> dataset = NM000103(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)