DS007823: eeg dataset, 169 subjects#
A COVID-19 survivors and close contacts EEG dataset
Citation: Ana Calzada-Reyes, Eduardo Aubert-Vázquez, Lidice Galán-García, Maria Luisa Bringas-Vega, Trinidad Virués-Alba, Lidia Charroó-Ruiz, Yanely Acosta-Imás, Mitchell Valdés-Sosa, Laura Perez-Mayo, Joel Gutiérrez-Gil, Antonio Caballero-Moreno, Miguel Angel Alvarez, Norge Santiesteban, Javier Vicente Sánchez-Lopez, Annette Valdés-Virués, Elba Elvira Varona-Galindo, Elizabeth Méndez-Parra, Joviana Castro-Valiente, Leyanis Ramos-Hernández, Mabel Whilby-Santiesteban, Thelma Luz Carrillo-Alfonso, Shahwar Yasir, Yu Kin, Peng Ren, Dezhong Yao, Luo Cheng, Roberto Rodriguez-Labrada, Pedro Valdés-Sosa (20). A COVID-19 survivors and close contacts EEG dataset. 10.18112/openneuro.ds007823.v1.0.1
169-participant EEG dataset — A COVID-19 survivors and close contacts EEG dataset.
Quickstart#
Install
pip install eegdash
Access the data
from eegdash.dataset import DS007823
dataset = DS007823(cache_dir="./data")
# Get the raw object of the first recording
raw = dataset.datasets[0].raw
print(raw.info)
Filter by subject
dataset = DS007823(cache_dir="./data", subject="01")
Advanced query
dataset = DS007823(
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{ds007823,
title = {A COVID-19 survivors and close contacts EEG dataset},
author = {Ana Calzada-Reyes and Eduardo Aubert-Vázquez and Lidice Galán-García and Maria Luisa Bringas-Vega and Trinidad Virués-Alba and Lidia Charroó-Ruiz and Yanely Acosta-Imás and Mitchell Valdés-Sosa and Laura Perez-Mayo and Joel Gutiérrez-Gil and Antonio Caballero-Moreno and Miguel Angel Alvarez and Norge Santiesteban and Javier Vicente Sánchez-Lopez and Annette Valdés-Virués and Elba Elvira Varona-Galindo and Elizabeth Méndez-Parra and Joviana Castro-Valiente and Leyanis Ramos-Hernández and Mabel Whilby-Santiesteban and Thelma Luz Carrillo-Alfonso and Shahwar Yasir and Yu Kin and Peng Ren and Dezhong Yao and Luo Cheng and Roberto Rodriguez-Labrada and Pedro Valdés-Sosa},
doi = {10.18112/openneuro.ds007823.v1.0.1},
url = {https://doi.org/10.18112/openneuro.ds007823.v1.0.1},
}
About This Dataset#
May 21st 2026
Cuban Neuroscience Center This is a dataset containing 169 EEGs including 83 healthy subjects and 86 COVID patients EEG was acquired using a 10-20 standard montage. Resting EEG was recorded for 8 minutes in all participants with eyes closed. Afterwards, it was also recorded 2 minutes of alternating closed and open eyes, followed by 2 minutes of recovery.
Cohort#
Dataset Statistics#
Sex composition
Channel counts: 22 ch (n=169 recordings)
Sampling frequencies: 200.0 Hz (n=169 recordings)
Total recording duration: 36 h
Signal · Electrodes & live trace#
Live trace viewer — sub-CUCOV038 · task-COVID
Showing one representative recording out of
169 subjects and 169 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 · 19 sensors — 19 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 |
A COVID-19 survivors and close contacts EEG dataset |
Author (year) |
— |
Canonical |
— |
Importable as |
|
Year |
20 |
Authors |
Ana Calzada-Reyes, Eduardo Aubert-Vázquez, Lidice Galán-García, Maria Luisa Bringas-Vega, Trinidad Virués-Alba, Lidia Charroó-Ruiz, Yanely Acosta-Imás, Mitchell Valdés-Sosa, Laura Perez-Mayo, Joel Gutiérrez-Gil, Antonio Caballero-Moreno, Miguel Angel Alvarez, Norge Santiesteban, Javier Vicente Sánchez-Lopez, Annette Valdés-Virués, Elba Elvira Varona-Galindo, Elizabeth Méndez-Parra, Joviana Castro-Valiente, Leyanis Ramos-Hernández, Mabel Whilby-Santiesteban, Thelma Luz Carrillo-Alfonso, Shahwar Yasir, Yu Kin, Peng Ren, Dezhong Yao, Luo Cheng, Roberto Rodriguez-Labrada, Pedro Valdés-Sosa |
License |
CC0 |
Citation / DOI |
|
Source links |
OpenNeuro | NeMAR | Source URL |
Copy-paste BibTeX
@dataset{ds007823,
title = {A COVID-19 survivors and close contacts EEG dataset},
author = {Ana Calzada-Reyes and Eduardo Aubert-Vázquez and Lidice Galán-García and Maria Luisa Bringas-Vega and Trinidad Virués-Alba and Lidia Charroó-Ruiz and Yanely Acosta-Imás and Mitchell Valdés-Sosa and Laura Perez-Mayo and Joel Gutiérrez-Gil and Antonio Caballero-Moreno and Miguel Angel Alvarez and Norge Santiesteban and Javier Vicente Sánchez-Lopez and Annette Valdés-Virués and Elba Elvira Varona-Galindo and Elizabeth Méndez-Parra and Joviana Castro-Valiente and Leyanis Ramos-Hernández and Mabel Whilby-Santiesteban and Thelma Luz Carrillo-Alfonso and Shahwar Yasir and Yu Kin and Peng Ren and Dezhong Yao and Luo Cheng and Roberto Rodriguez-Labrada and Pedro Valdés-Sosa},
doi = {10.18112/openneuro.ds007823.v1.0.1},
url = {https://doi.org/10.18112/openneuro.ds007823.v1.0.1},
}
API Reference#
eegdash.datasetEEGDashDataset- class eegdash.dataset.DS007823(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#
A COVID-19 survivors and close contacts EEG dataset
- Study:
ds007823(OpenNeuro)- Author (year):
nan- Canonical:
—
Also importable as:
DS007823,nan.Modality:
eeg. Subjects: 169; recordings: 169; 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
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/ds007823 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds007823 DOI: https://doi.org/10.18112/openneuro.ds007823.v1.0.1
Examples
>>> from eegdash.dataset import DS007823 >>> dataset = DS007823(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.pytorch
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 ds007823 to reproduce the tutorial on this dataset.
Citation
Ana Calzada-Reyes, Eduardo Aubert-Vázquez, Lidice Galán-García, Maria Luisa Bringas-Vega, Trinidad Virués-Alba, … (20). A COVID-19 survivors and close contacts EEG dataset. 10.18112/openneuro.ds007823.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.ds007823.v1.0.1.
See Also#
eegdash.dataset.EEGDashDataseteegdash.dataset