NM000103: eeg dataset, 447 subjects#
Healthy Brain Network EEG - Not for Commercial Use
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
447-participant EEG dataset — Healthy Brain Network EEG - Not for Commercial Use.
Quickstart#
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)
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'])
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#
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).
This dataset comprises electroencephalogram (EEG) data and behavioral responses collected during EEG experiments from participants involved in the HBN project.
Overview
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.
Cohort#
Dataset Statistics#
Age distribution by gender (n=447, range 5–22 yr, mean 10.3 yr)
Sex composition
Channel counts: 129 ch (n=3522 recordings)
Sampling frequencies: 500.0 Hz (n=3522 recordings)
Total recording duration: 285 h
Signal · Electrodes & live trace#
Live trace viewer — sub-NDARAJ807UYR · task-RestingState
Showing one representative recording out of
447 subjects and 3522 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
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.
Full dataset metadata table
Dataset ID |
|
Title |
Healthy Brain Network EEG - Not for Commercial Use |
Author (year) |
|
Canonical |
— |
Importable as |
|
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 |
|
Source links |
OpenNeuro | NeMAR | Source URL |
Copy-paste 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},
}
API Reference#
eegdash.datasetEEGDashDatasetNM000103 · Shirazi2017eegdash/dataset/registry.py · [source ↗]- class eegdash.dataset.NM000103(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#
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
Notes
Each item is a recording; recording-level metadata are available via
dataset.description.querysupports MongoDB-style filters on fields inALLOWED_QUERY_FIELDSand 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)
- 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.
BaseDataset from braindecode — windowed via create_windows_from_events.braindecodeDataLoader; supports parallel workers and on-the-fly augmentations.pytorch
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 nm000103 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 EEG - Not for Commercial Use. 10.82901/nemar.nm000103
Provenance
¹Contributed to nemar in BIDS format.
²Curated & ingested by the EEGDash catalog; see CITATION.cff for canonical reference.
³Persistent identifier: 10.82901/nemar.nm000103.
See Also#
eegdash.dataset.EEGDashDataseteegdash.dataset