DS004577: eeg dataset, 103 subjects#

Dataset containing resting EEG for a sample of 103 normal infants in the first year of life

Access recordings and metadata through EEGDash.

Citation: Thalía Harmony (Neurodevelopment Research Unit; Instituto de Neurobiología; Universidad Nacional Autónoma de México), Gloria Otero-Ojeda (Facultad de Medicina; Universidad Autónoma del Estado de México), Eduardo Aubert (Centro de Neurociencias de Cuba), Thalía Fernández (Neurodevelopment Research Unit; Instituto de Neurobiología; Universidad Nacional Autónoma de México), Lourdes Cubero-Rego (Neurodevelopment Research Unit; Instituto de Neurobiología; Universidad Nacional Autónoma de México) (2023). Dataset containing resting EEG for a sample of 103 normal infants in the first year of life. 10.18112/openneuro.ds004577.v1.0.1

Modality: eeg Subjects: 103 Recordings: 130 License: CC0 Source: openneuro Citations: 3.0

Metadata: Complete (100%)

Quickstart#

Get Started

Install

pip install eegdash

Access the data

from eegdash.dataset import DS004577

dataset = DS004577(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 = DS004577(cache_dir="./data", subject="01")

Advanced query

dataset = DS004577(
    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{ds004577,
  title = {Dataset containing resting EEG for a sample of 103 normal infants in the first year of life},
  author = {Thalía Harmony (Neurodevelopment Research Unit; Instituto de Neurobiología; Universidad Nacional Autónoma de México) and Gloria Otero-Ojeda (Facultad de Medicina; Universidad Autónoma del Estado de México) and Eduardo Aubert (Centro de Neurociencias de Cuba) and Thalía Fernández (Neurodevelopment Research Unit; Instituto de Neurobiología; Universidad Nacional Autónoma de México) and Lourdes Cubero-Rego (Neurodevelopment Research Unit; Instituto de Neurobiología; Universidad Nacional Autónoma de México)},
  doi = {10.18112/openneuro.ds004577.v1.0.1},
  url = {https://doi.org/10.18112/openneuro.ds004577.v1.0.1},
}

About This Dataset#

May 25th 2023 Neurodevelopment Research Unit, Instituto de Neurobiología, Universidad Nacional Autónoma de México This is a dataset containing resting EEG for a sample of 103 normal infants (41 female and 62 male) in the first year of life. 81 subjects with 1 EEG recording 18 subjects with 2 EEG recordings 3 subjects with 3 EEG recording 1 subject with 4 EEG recordings 130 EEG recordings in total distributed in 4 sessions

Dataset Information#

Dataset ID	`DS004577`
Title	Dataset containing resting EEG for a sample of 103 normal infants in the first year of life
Author (year)	`Unit2023`
Canonical	—
Importable as	`DS004577`, `Unit2023`
Year	2023
Authors	Thalía Harmony (Neurodevelopment Research Unit; Instituto de Neurobiología; Universidad Nacional Autónoma de México), Gloria Otero-Ojeda (Facultad de Medicina; Universidad Autónoma del Estado de México), Eduardo Aubert (Centro de Neurociencias de Cuba), Thalía Fernández (Neurodevelopment Research Unit; Instituto de Neurobiología; Universidad Nacional Autónoma de México), Lourdes Cubero-Rego (Neurodevelopment Research Unit; Instituto de Neurobiología; Universidad Nacional Autónoma de México)
License	CC0
Citation / DOI	doi:10.18112/openneuro.ds004577.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: 103
Recordings: 130
Tasks: 1

Channels & sampling rate

Channels: 19 (106), 24 (23), 21
Sampling rate (Hz): 200.0
Duration (hours): 22.973859722222223

Electrode Layout#

Electrode layout — EEG · 19 sensors — 19 channels

Dataset Statistics#

Sex distribution

41

62

Female Male Total: 103

Channel counts (ch)

192124

Sampling frequencies: 200.0 Hz (n=130 recordings)

Total recording duration: 22 h 58 min

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 DS004577 class to access this dataset programmatically.

class eegdash.dataset.DS004577(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#

Bases: EEGDashDataset

Dataset containing resting EEG for a sample of 103 normal infants in the first year of life

Study:: ds004577 (OpenNeuro)
Author (year):: Unit2023
Canonical:: —

Also importable as: DS004577, Unit2023.

Modality: eeg. Subjects: 103; recordings: 130; tasks: 1.

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/ds004577 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds004577 DOI: https://doi.org/10.18112/openneuro.ds004577.v1.0.1 NEMAR citation count: 3

Examples

>>> from eegdash.dataset import DS004577
>>> dataset = DS004577(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)