EEGdash›OpenNeuro›DS006466
Iss. 6466 · 66 subjects · 1257 recordings · CC0
Dataset Brief · HeartBEAM

DS006466: eeg dataset, 66 subjects#

HeartBEAM: Older Adult Resting State and Auditory Oddball Task EEG Data

Citation: Andy Jeesu Kim, Santiago Morales, Joshua Senior, Mara Mather (2024). HeartBEAM: Older Adult Resting State and Auditory Oddball Task EEG Data. 10.18112/openneuro.ds006466.v1.0.1

66-participant EEG dataset — HeartBEAM: Older Adult Resting State and Auditory Oddball Task EEG Data.

Data & curation Andy Jeesu Kim · Santiago Morales · Joshua Senior · Mara Mather
Year 2024 · Distributed via OpenNeuro
EEG · 65 ch1000 HzBIDS 1.1.16 tasks2 sessionsHealthyAuditoryAttention
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#

Install

pip install eegdash

Access the data

from eegdash.dataset import DS006466

dataset = DS006466(cache_dir="./data")
# Get the raw object of the first recording
raw = dataset.datasets[0].raw
print(raw.info)

Filter by subject

dataset = DS006466(cache_dir="./data", subject="01")

Advanced query

dataset = DS006466(
    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{ds006466,
  title = {HeartBEAM: Older Adult Resting State and Auditory Oddball Task EEG Data},
  author = {Andy Jeesu Kim and Santiago Morales and Joshua Senior and Mara Mather},
  doi = {10.18112/openneuro.ds006466.v1.0.1},
  url = {https://doi.org/10.18112/openneuro.ds006466.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.

Older 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=1257 recordings)

Sampling frequencies: 1000.0 Hz (n=1257 recordings)

Total recording duration: 131 h

§ 04Signal · Electrodes & trace

Signal · Electrodes & live trace#

Fig. 01 Signal & montage 65 ch · EEG · 1000 Hz · 66 subjects, 1257 recordings
Live trace viewer — sub-FivePly · ses-pre · task-active · run-1

Showing one representative recording out of 66 subjects and 1257 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.

NEMAR pipeline statistics — DS006466
HED event descriptors word cloud HED event descriptors word cloud — DS006466
§ 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…
Full dataset metadata table

Dataset ID

DS006466

Title

HeartBEAM: Older Adult Resting State and Auditory Oddball Task EEG Data

Author (year)

Kim2025_HeartBEAM_Older_Adult

Canonical

—

Importable as

DS006466, Kim2025_HeartBEAM_Older_Adult

Year

2024

Authors

Andy Jeesu Kim, Santiago Morales, Joshua Senior, Mara Mather

License

CC0

Citation / DOI

doi:10.18112/openneuro.ds006466.v1.0.1

Source links

OpenNeuro | NeMAR | Source URL
Copy-paste BibTeX
@dataset{ds006466,
  title = {HeartBEAM: Older Adult Resting State and Auditory Oddball Task EEG Data},
  author = {Andy Jeesu Kim and Santiago Morales and Joshua Senior and Mara Mather},
  doi = {10.18112/openneuro.ds006466.v1.0.1},
  url = {https://doi.org/10.18112/openneuro.ds006466.v1.0.1},
}
§ 06API · Programmatic access

API Reference#

Signature
eegdash.dataset
class
eegdash.dataset.DS006466(cache_dir, query=None, s3_bucket=None, **kwargs)
Bases: EEGDashDataset
Author (year)Kim2025_HeartBEAM_Older_Adult
Canonical—
Importable asDS006466 · Kim2025_HeartBEAM_Older_Adult
Sourceeegdash/dataset/registry.py · [source ↗]
class eegdash.dataset.DS006466(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#

HeartBEAM: Older Adult Resting State and Auditory Oddball Task EEG Data

Study:

ds006466 (OpenNeuro)

Author (year):

Kim2025_HeartBEAM_Older_Adult

Canonical:

—

Also importable as: DS006466, Kim2025_HeartBEAM_Older_Adult.

Modality: eeg; Experiment type: Attention; Subject type: Healthy. Subjects: 66; recordings: 1257; tasks: 6.

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/ds006466 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds006466 DOI: https://doi.org/10.18112/openneuro.ds006466.v1.0.1

Examples

>>> from eegdash.dataset import DS006466
>>> dataset = DS006466(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/ds006466 · pull with datasets.load_dataset("EEGDash/ds006466").huggingface
Croissant 1.0Machine-readable JSON-LD descriptor — DS006466.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 ds006466 to reproduce the tutorial on this dataset.

Citation

Andy Jeesu Kim, Santiago Morales, Joshua Senior, Mara Mather (2024). HeartBEAM: Older Adult Resting State and Auditory Oddball Task EEG Data. 10.18112/openneuro.ds006466.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.ds006466.v1.0.1.

BIDS
BIDS 1.1.1
Sidecars
events · eeg.json
Machine-readable
schema.org/Dataset · Croissant

See Also#