EEGdash›OpenNeuro›DS006480

Iss. 6480 · 68 subjects · 68 recordings · CC0

Dataset Brief · Young Adult Resting State and Auditory Oddball Task EEG Data

DS006480: eeg dataset, 68 subjects#

Young Adult Resting State and Auditory Oddball Task EEG Data

Citation: Andy Jeesu Kim, Mara Mather, Santiago Morales, Joshua Senior (2024). Young Adult Resting State and Auditory Oddball Task EEG Data. 10.18112/openneuro.ds006480.v1.0.1

68-participant EEG dataset — Young Adult Resting State and Auditory Oddball Task EEG Data.

Data & curation Andy Jeesu Kim · Mara Mather · Santiago Morales · Joshua Senior
Year 2024 · Distributed via OpenNeuro

EEG · 65 ch1000 HzBIDS 1.8.0Task · oddballHealthyAuditoryAttention

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 DS006480

dataset = DS006480(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 = DS006480(cache_dir="./data", subject="01")

Advanced query

dataset = DS006480(
    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{ds006480,
  title = {Young Adult Resting State and Auditory Oddball Task EEG Data},
  author = {Andy Jeesu Kim and Mara Mather and Santiago Morales and Joshua Senior},
  doi = {10.18112/openneuro.ds006480.v1.0.1},
  url = {https://doi.org/10.18112/openneuro.ds006480.v1.0.1},
}

§ 02Study · The README

About This Dataset#

100 - 5 minutes eyes open resting, control condition, begin

101 - 5 minutes eyes open resting, control condition, end 102 - 5 minutes eyes closed resting, control condition, begin 103 - 5 minutes eyes closed resting, control condition, end 104 - passive auditory oddball task, control condition, run begin 105 - passive auditory oddball task, control condition, trial start 106 - passive auditory oddball task, control condition, standard tone 107 - passive auditory oddball task, control condition, target tone 108 - passive auditory oddball task, control condition, distractor tone 109 - passive auditory oddball task, control condition, trial end 110 - passive auditory oddball task, control condition, run end 111 - active auditory oddball task, control condition, run begin 112 - active auditory oddball task, control condition, trial start 113 - active auditory oddball task, control condition, standard tone 114 - active auditory oddball task, control condition, target tone 115 - active auditory oddball task, control condition, distractor tone 116 - active auditory oddball task, control condition, start of response period 117 - active auditory oddball task, control condition, manual button response recorded 118 - active auditory oddball task, control condition, run end 119 - 5 minutes eyes open resting, shock condition, begin 120 - 5 minutes eyes open resting, shock condition, end 121 - 5 minutes eyes closed resting, shock condition, begin 122 - 5 minutes eyes closed resting, shock condition, end 123 - passive auditory oddball task, shock condition, run begin 124 - passive auditory oddball task, shock condition, trial start 125 - passive auditory oddball task, shock condition, standard tone 126 - passive auditory oddball task, shock condition, target tone 127 - passive auditory oddball task, shock condition, distractor tone 128 - passive auditory oddball task, shock condition, trial end 129 - passive auditory oddball task, shock condition, run end 130 - active auditory oddball task, shock condition, run begin 131 - active auditory oddball task, shock condition, trial start 132 - active auditory oddball task, shock condition, standard tone 133 - active auditory oddball task, shock condition, target tone 134 - active auditory oddball task, shock condition, distractor tone 135 - active auditory oddball task, shock condition, start of response period 136 - active auditory oddball task, shock condition, manual button response recorded 137 - active auditory oddball task, shock condition, run end

Nashiro, K., Yoo, H. J., Cho, C., Kim, A. J., Nasseri, P., Min, J., … & Mather, M. (2024). Heart rate and breathing effects on attention and memory (HeartBEAM): Study protocol for a randomized controlled trial in older adults. Trials, 25(1), 190.

Young Adult Resting State and Auditory Oddball Task EEG Data

What is included

This dataset includes resting state and auditory oddball task EEG data for two conditions: control and arousal (under threat of unpredictable shock).

Event labels

Kim, A. J., Morales, S., Senior, J., & Mather, M. (2025). Electroencephalography, pupillometry, and behavioral evidence for locus coeruleus-noradrenaline system related tonic hyperactivity in older adults. Preprint: doi.org/10.1101/2025.10.02.680040

§ 03Cohort · Participants

Cohort#

Dataset Statistics#

Channel counts: 65 ch (n=68 recordings)

Sampling frequencies: 1000.0 Hz (n=68 recordings)

Total recording duration: 71 h

§ 04Signal · Electrodes & trace

Signal · Electrodes & live trace#

Fig. 01 Signal & montage 65 ch · EEG · 1000 Hz · 68 subjects, 68 recordings

Live trace viewer — sub-13 · task-oddball

Showing one representative recording out of 68 subjects and 68 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 · 65 sensors — 65 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.DS006480(cache_dir, query=None, s3_bucket=None, **kwargs)

Bases: EEGDashDataset

Author (year)Kim2025_Young_Adult_Resting

Canonical—

Importable asDS006480 · Kim2025_Young_Adult_Resting

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

class eegdash.dataset.DS006480(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#

Young Adult Resting State and Auditory Oddball Task EEG Data

Study:: ds006480 (OpenNeuro)
Author (year):: Kim2025_Young_Adult_Resting
Canonical:: —

Also importable as: DS006480, Kim2025_Young_Adult_Resting.

Modality: eeg; Experiment type: Attention; Subject type: Healthy. Subjects: 68; recordings: 68; 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/ds006480 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds006480 DOI: https://doi.org/10.18112/openneuro.ds006480.v1.0.1

Examples

>>> from eegdash.dataset import DS006480
>>> dataset = DS006480(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 FacePre-bundled mirror at EEGDash/ds006480 · pull with datasets.load_dataset("EEGDash/ds006480").huggingface

Croissant 1.0Machine-readable JSON-LD descriptor — DS006480.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 ds006480 to reproduce the tutorial on this dataset.

Citation

Andy Jeesu Kim, Mara Mather, Santiago Morales, Joshua Senior (2024). Young Adult Resting State and Auditory Oddball Task EEG Data. 10.18112/openneuro.ds006480.v1.0.1

Provenance

¹Contributed to openneuro in BIDS format.

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

³Persistent identifier: 10.18112/openneuro.ds006480.v1.0.1.

Related & sibling datasets

DS007322EEG · 57 subj DS003620EEG · 44 subj DS005345EEG · 26 subj DS004033EEG · 18 subj DS004940EEG · 22 subj

+ 1 more — see See Also below →

BIDS

BIDS 1.8.0

Sidecars

events · channels · electrodes · coordsystem · eeg.json

Provenance

CC0 · 10.18112/openneuro.ds006480.v1.0.1

Machine-readable

schema.org/Dataset · Croissant

Mirrors

OpenNeuro · NEMAR · HuggingFace

Dataset ID	`DS006480`
Title	Young Adult Resting State and Auditory Oddball Task EEG Data
Author (year)	`Kim2025_Young_Adult_Resting`
Canonical	—
Importable as	`DS006480`, `Kim2025_Young_Adult_Resting`
Year	2024
Authors	Andy Jeesu Kim, Mara Mather, Santiago Morales, Joshua Senior
License	CC0
Citation / DOI	doi:10.18112/openneuro.ds006480.v1.0.1
Source links	OpenNeuro \| NeMAR \| Source URL

DS006480: eeg dataset, 68 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#