DS003690: eeg dataset, 75 subjects#
EEG, ECG and pupil data from young and older adults: rest and auditory cued reaction time tasks
Citation: Maria J. Ribeiro, Miguel Castelo-Branco (—). EEG, ECG and pupil data from young and older adults: rest and auditory cued reaction time tasks. 10.18112/openneuro.ds003690.v1.0.0
75-participant EEG dataset — EEG, ECG and pupil data from young and older adults: rest and auditory cued reaction time tasks.
Quickstart#
Install
pip install eegdash
Access the data
from eegdash.dataset import DS003690
dataset = DS003690(cache_dir="./data")
# Get the raw object of the first recording
raw = dataset.datasets[0].raw
print(raw.info)
Filter by subject
dataset = DS003690(cache_dir="./data", subject="01")
Advanced query
dataset = DS003690(
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{ds003690,
title = {EEG, ECG and pupil data from young and older adults: rest and auditory cued reaction time tasks},
author = {Maria J. Ribeiro and Miguel Castelo-Branco},
doi = {10.18112/openneuro.ds003690.v1.0.0},
url = {https://doi.org/10.18112/openneuro.ds003690.v1.0.0},
}
About This Dataset#
Age-related differences in EEG, ECG and pupilography during auditory cued reaction time tasks
In this study, we acquired the electroencephalogram (EEG), pupilogram and electrocardiogram (ECG) while a group of young (N = 36) and a group of older (N = 39) adults were engaged in auditory cued reaction time tasks (active tasks) or passively listening to the auditory stimulus used as temporal cue, presented with the same frequency as in the active tasks (passive task - 4 minutes acquired at the beginning of the session).
The active tasks were a cued simple reaction time task and a cued go/no-go task. In the active tasks, 16% of the trials were cue only trials (the cue was presented but no target followed).
The order of the active tasks was counterbalanced across participants and were acquired in two runs of 8 minutes per task. In each task, we acquired 120 trials. In the simple reaction time task, 100 trials were cue-target trials and 20 trials were cue-only. In the go/no-go task, 80 trials were cue-go trials, 20 were cue-no-go trials, and 20 trials were cue-only trials.
Participants were fixating a grey computer screen with a lighter grey fixation cross at the center. The auditory stimuli were single-frequency signals (pure tones) with duration 250 ms, with the following frequencies: cue 1500 Hz; go stimulus 1700 Hz; no-go stimulus 1300 Hz; and error feedback signal 1000 Hz. The sounds were played at around 67 dB(A) from a hi-fi speakers system. All stimuli were suprathreshold. EEG signal was recorded using a 64-channel Neuroscan system with scalp electrodes placed according to the International 10-20 electrode placement standard, with reference between the electrodes CPz and Cz and ground between FPz and Fz. Acquisition rate was 500 Hz. Vertical and horizontal electrooculograms were recorded to monitor eye movements and blinks. Bipolar electrocardiogram (ECG) electrodes were placed on the chest. During data acquisition, the participants head was stabilized with a chin and forehead rest. Consequently, the electrodes on the forehead, FP1, FPz, and FP2, displayed signal fluctuation artifacts due to the pressure on the forehead rest. These were excluded from the recordings.
Electrode positions were measured using a 3D-digitizer Fastrak (Polhemus, VT, USA) and imported into the EEGLAB files. Pupil data was acquired with iView X Hi-Speed 1250 system from SMI with a sampling rate of 240 Hz. Pupil data was imported into the EEG dataset with the EYE-EEG EEGLAB plugin.
Synchronized EEG, ECG and pupil data are included in separate channels in the EEGLAB .set files.
Cohort#
Dataset Statistics#
Age distribution by gender (n=75, range 19–70 yr, mean 42.5 yr)
Sex composition
Channel counts (ch)
Sampling frequencies: 500.0 Hz (n=375 recordings)
Total recording duration: 47 h
Signal · Electrodes & live trace#
Live trace viewer — sub-AB37 · task-simpleRT · run-1
Showing one representative recording out of
75 subjects and 375 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 · 64 sensors — 64 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 |
EEG, ECG and pupil data from young and older adults: rest and auditory cued reaction time tasks |
Author (year) |
|
Canonical |
— |
Importable as |
|
Year |
— |
Authors |
Maria J. Ribeiro, Miguel Castelo-Branco |
License |
CC0 |
Citation / DOI |
|
Source links |
OpenNeuro | NeMAR | Source URL |
Copy-paste BibTeX
@dataset{ds003690,
title = {EEG, ECG and pupil data from young and older adults: rest and auditory cued reaction time tasks},
author = {Maria J. Ribeiro and Miguel Castelo-Branco},
doi = {10.18112/openneuro.ds003690.v1.0.0},
url = {https://doi.org/10.18112/openneuro.ds003690.v1.0.0},
}
API Reference#
eegdash.datasetEEGDashDatasetDS003690 · Ribeiro2021eegdash/dataset/registry.py · [source ↗]- class eegdash.dataset.DS003690(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#
EEG, ECG and pupil data from young and older adults: rest and auditory cued reaction time tasks
- Study:
ds003690(OpenNeuro)- Author (year):
Ribeiro2021- Canonical:
—
Also importable as:
DS003690,Ribeiro2021.Modality:
eeg. Subjects: 75; recordings: 375; tasks: 3.- 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/ds003690 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds003690 DOI: https://doi.org/10.18112/openneuro.ds003690.v1.0.0 NEMAR citation count: 5
Examples
>>> from eegdash.dataset import DS003690 >>> dataset = DS003690(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.pytorchdatasets.load_dataset("EEGDash/ds003690").huggingface
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 ds003690 to reproduce the tutorial on this dataset.
Citation
Maria J. Ribeiro, Miguel Castelo-Branco (n.d.). EEG, ECG and pupil data from young and older adults: rest and auditory cued reaction time tasks. 10.18112/openneuro.ds003690.v1.0.0
Provenance
¹Contributed to openneuro in BIDS format.
²Curated & ingested by the EEGDash catalog; see CITATION.cff for canonical reference.
³Persistent identifier: 10.18112/openneuro.ds003690.v1.0.0.
See Also#
eegdash.dataset.EEGDashDataseteegdash.dataset