DS003838: eeg dataset, 65 subjects#
EEG, pupillometry, ECG and photoplethysmography, and behavioral data in the digit span task and rest
Citation: Yuri G. Pavlov, Dauren Kasanov, Alexandra I. Kosachenko, Alexander I. Kotyusov (—). EEG, pupillometry, ECG and photoplethysmography, and behavioral data in the digit span task and rest. 10.18112/openneuro.ds003838.v1.0.6
65-participant EEG dataset — EEG, pupillometry, ECG and photoplethysmography, and behavioral data in the digit span task and rest.
Quickstart#
Install
pip install eegdash
Access the data
from eegdash.dataset import DS003838
dataset = DS003838(cache_dir="./data")
# Get the raw object of the first recording
raw = dataset.datasets[0].raw
print(raw.info)
Filter by subject
dataset = DS003838(cache_dir="./data", subject="01")
Advanced query
dataset = DS003838(
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{ds003838,
title = {EEG, pupillometry, ECG and photoplethysmography, and behavioral data in the digit span task and rest},
author = {Yuri G. Pavlov and Dauren Kasanov and Alexandra I. Kosachenko and Alexander I. Kotyusov},
doi = {10.18112/openneuro.ds003838.v1.0.6},
url = {https://doi.org/10.18112/openneuro.ds003838.v1.0.6},
}
About This Dataset#
This dataset consists of raw 64-channel EEG, cardiovascular (electrocardiography and photoplethysmography), and pupillometry data from 86 human participants during 4 minutes of eyes-closed resting and during performance of a classic working memory task – digit span task with serial recall. The participants either memorized (memory) or just listened to (control condition) sequences of 5, 9, or 13 digits presented auditorily with 2 second stimulus onset asynchrony. The dataset can be used for (1) developing algorithms for cognitive load discrimination and detection of cognitive overload; (2) studying neural (event-related potentials and brain oscillations) and peripheral physiological (electrocardiography, photoplethysmography, and pupillometry) signals during encoding and maintenance of each sequentially presented memory item in a fine time scale; (3) correlating cognitive load and individual differences in working memory to neural and peripheral physiology, and studying the relationship between the physiological signals; (4) integration of the physiological findings with the vast knowledge coming from behavioral studies of verbal working memory in simple span paradigms.
EEG, pupillometry, ECG and photoplethysmography, and behavioral data are stored separately in corresponding folders. Each data record can consist of four data folders: beh - behavioral data: correctness of the recall in the memory trials ecg - electrocardiography (ECG) and photoplethysmography (PPG) data eeg - EEG data pupil - pupillometry and eye-tracking data Some of the participants had some physiological data missing: sub-017, sub-094 have no pupillometry data sub-017, sub-037, sub-066 have no ECG and PPG data sub-013, sub-014, sub-015, sub-016, sub-017, sub-018, sub-019, sub-020, sub-021, sub-022, sub-023, sub-024, sub-025, sub-026, sub-027, sub-028, sub-029, sub-030, sub-031, sub-037, sub-066 have no EEG data
Cohort#
Dataset Statistics#
Age distribution by gender (n=65, range 18–37 yr, mean 20.5 yr)
Sex composition
Channel counts: 63 ch (n=130 recordings)
Sampling frequencies: 1000.0 Hz (n=130 recordings)
Total recording duration: 142 h
Signal · Electrodes & live trace#
Live trace viewer — sub-088 · task-memory
Showing one representative recording out of
65 subjects and 130 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 · 63 sensors — 63 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, pupillometry, ECG and photoplethysmography, and behavioral data in the digit span task and rest |
Author (year) |
|
Canonical |
— |
Importable as |
|
Year |
— |
Authors |
Yuri G. Pavlov, Dauren Kasanov, Alexandra I. Kosachenko, Alexander I. Kotyusov |
License |
CC0 |
Citation / DOI |
|
Source links |
OpenNeuro | NeMAR | Source URL |
Copy-paste BibTeX
@dataset{ds003838,
title = {EEG, pupillometry, ECG and photoplethysmography, and behavioral data in the digit span task and rest},
author = {Yuri G. Pavlov and Dauren Kasanov and Alexandra I. Kosachenko and Alexander I. Kotyusov},
doi = {10.18112/openneuro.ds003838.v1.0.6},
url = {https://doi.org/10.18112/openneuro.ds003838.v1.0.6},
}
API Reference#
eegdash.datasetEEGDashDatasetDS003838 · Pavlov2021_pupillometryeegdash/dataset/registry.py · [source ↗]- class eegdash.dataset.DS003838(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#
EEG, pupillometry, ECG and photoplethysmography, and behavioral data in the digit span task and rest
- Study:
ds003838(OpenNeuro)- Author (year):
Pavlov2021_pupillometry- Canonical:
—
Also importable as:
DS003838,Pavlov2021_pupillometry.Modality:
eeg. Subjects: 65; recordings: 130; tasks: 2.- 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/ds003838 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds003838 DOI: https://doi.org/10.18112/openneuro.ds003838.v1.0.6 NEMAR citation count: 7
Examples
>>> from eegdash.dataset import DS003838 >>> dataset = DS003838(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/ds003838").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 ds003838 to reproduce the tutorial on this dataset.
Citation
Yuri G. Pavlov, Dauren Kasanov, Alexandra I. Kosachenko, Alexander I. Kotyusov (n.d.). EEG, pupillometry, ECG and photoplethysmography, and behavioral data in the digit span task and rest. 10.18112/openneuro.ds003838.v1.0.6
Provenance
¹Contributed to openneuro in BIDS format.
²Curated & ingested by the EEGDash catalog; see CITATION.cff for canonical reference.
³Persistent identifier: 10.18112/openneuro.ds003838.v1.0.6.
See Also#
eegdash.dataset.EEGDashDataseteegdash.dataset