DS007358: eeg dataset, 2000 subjects#
A subset of large-scale EEG dataset (India + Tanzania)
Citation: John Mary Vianney, Shailender Swaminathan, Jennifer Jane Newson, Dhanya Parameshwaran, Narayan Puthanmadam Subramaniyam, Swaeta Singha Roy, Revocatus Machunda, Achiwa Sapuli, Santanu Pramanik, John Victor Arun Kumar, Pramod Tiwari, G. Nelson Mathews Mathuram, Laurent Boniface Bembeleza, Joyce Philemon Laiser, Winifrida Julius Luhwago, Theresia Pastory Maduka, John Olais Mollel, Neema Gadiely Mollel, Adella Aloys Mugizi, Isaac Lwaga Mwamakula, Raymond Edwin Rweyemamu, Upendo Firimini Samweli, James Isaac Simpito, Kelvin Ewald Shirima, Anand Anbalagan, Suresh Kumar Arumugam, Vinitha Dhanapal, Kanimozhi Gunasekaran, Neelu Kashyap, Dheeraj Kumar, Durgesh Pandey, Poonam Pandey, Arunkumar Panneerselvam, Sonam Rai, Porselvi Rajendran, Santhoshkumar Sekar, Oliazhagan Sivalingam, Prahalad Soni, Pushpkala Soni, Tara C. Thiagarajan (2019). A subset of large-scale EEG dataset (India + Tanzania). 10.18112/openneuro.ds007358.v1.0.0
2000-participant EEG dataset — A subset of large-scale EEG dataset (India + Tanzania).
Quickstart#
Install
pip install eegdash
Access the data
from eegdash.dataset import DS007358
dataset = DS007358(cache_dir="./data")
# Get the raw object of the first recording
raw = dataset.datasets[0].raw
print(raw.info)
Filter by subject
dataset = DS007358(cache_dir="./data", subject="01")
Advanced query
dataset = DS007358(
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{ds007358,
title = {A subset of large-scale EEG dataset (India + Tanzania)},
author = {John Mary Vianney and Shailender Swaminathan and Jennifer Jane Newson and Dhanya Parameshwaran and Narayan Puthanmadam Subramaniyam and Swaeta Singha Roy and Revocatus Machunda and Achiwa Sapuli and Santanu Pramanik and John Victor Arun Kumar and Pramod Tiwari and G. Nelson Mathews Mathuram and Laurent Boniface Bembeleza and Joyce Philemon Laiser and Winifrida Julius Luhwago and Theresia Pastory Maduka and John Olais Mollel and Neema Gadiely Mollel and Adella Aloys Mugizi and Isaac Lwaga Mwamakula and Raymond Edwin Rweyemamu and Upendo Firimini Samweli and James Isaac Simpito and Kelvin Ewald Shirima and Anand Anbalagan and Suresh Kumar Arumugam and Vinitha Dhanapal and Kanimozhi Gunasekaran and Neelu Kashyap and Dheeraj Kumar and Durgesh Pandey and Poonam Pandey and Arunkumar Panneerselvam and Sonam Rai and Porselvi Rajendran and Santhoshkumar Sekar and Oliazhagan Sivalingam and Prahalad Soni and Pushpkala Soni and Tara C. Thiagarajan},
doi = {10.18112/openneuro.ds007358.v1.0.0},
url = {https://doi.org/10.18112/openneuro.ds007358.v1.0.0},
}
About This Dataset#
There is a growing imperative to understand the neurophysiological impact of our rapidly changing and diverse technological, social, chemical, and physical environments. To untangle the multidimensional and interacting effects requires data at scale across diverse populations, taking measurement out of a controlled lab environment and into the field. Electroencephalography (EEG), which has correlates with various environmental factors as well as cognitive and mental health outcomes, has the advantage of both portability and cost-effectiveness for this purpose. However, with numerous field researchers spread across diverse locations, data quality issues and researcher idle time due to insufficient participants can quickly become unmanageable and expensive problems. In programs we have established in India and Tanzania, we demonstrate that with appropriate training, structured teams, and daily automated analysis and feedback on data quality, nonspecialists can reliably collect EEG data alongside various survey and assessments with consistently high throughput and quality. Over a 30 week period, research teams were able to maintain an average of 25.6 participants per week, collecting data from a diverse sample of 7,933 participants ranging from Hadzabe hunter-gatherers to office workers. Furthermore, data quality, computed on the first 5,831 records using two common methods, PREP and FASTER, was comparable to benchmark datasets from controlled lab conditions. Altogether this resulted in a cost per participant of under $50, a fraction of the cost typical of such data collection, opening up the possibility for large-scale programs particularly in low- and middle-income countries.
A subset of large-scale EEG recordings from India and Tanzania are uploaded here along with metadata like age, mental health quotient (MHQ) score, income and sex. This BIDS dataset was generated using MNE-BIDS from EDF source files.
Vianney JM, Swaminathan S, Newson JJ, Parameshwaran D, Subramaniyam NP, Roy SS, Machunda R, Sapuli A, Pramanik S, Kumar JV, Tiwari P. EEG Data Quality in Large-Scale Field Studies in India and Tanzania. Eneuro. 2025 Jul 1;12(7).
Newson JJ, Pastukh V, Thiagarajan TC. Assessment of population well-being with the Mental Health Quotient: validation study. JMIR Mental Health. 2022 Apr 20;9(4):e34105. Appelhoff, S., Sanderson, M., Brooks, T., Vliet, M., Quentin, R., Holdgraf, C., Chaumon, M., Mikulan, E., Tavabi, K., Höchenberger, R., Welke, D., Brunner, C., Rockhill, A., Larson, E., Gramfort, A. and Jas, M. (2019). MNE-BIDS: Organizing electrophysiological data into the BIDS format and facilitating their analysis. Journal of Open Source Software 4: (1896).https://doi.org/10.21105/joss.01896 Pernet, C. R., Appelhoff, S., Gorgolewski, K. J., Flandin, G., Phillips, C., Delorme, A., Oostenveld, R. (2019). EEG-BIDS, an extension to the brain imaging data structure for electroencephalography. Scientific Data, 6, 103.https://doi.org/10.1038/s41597-019-0104-8
Cohort#
Dataset Statistics#
Sex composition
Channel counts (ch)
Sampling frequencies (Hz)
Total recording duration: 276 h
Signal · Electrodes & live trace#
Live trace viewer — sub-UPSA6082 · task-pc
Showing one representative recording out of
2000 subjects and 6000 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.
No scalp electrode layout is currently indexed for this dataset. Once the eegdash montage registry ingests it, the interactive viewer will appear here automatically.
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 subset of large-scale EEG dataset (India + Tanzania) |
Author (year) |
|
Canonical |
— |
Importable as |
|
Year |
2019 |
Authors |
John Mary Vianney, Shailender Swaminathan, Jennifer Jane Newson, Dhanya Parameshwaran, Narayan Puthanmadam Subramaniyam, Swaeta Singha Roy, Revocatus Machunda, Achiwa Sapuli, Santanu Pramanik, John Victor Arun Kumar, Pramod Tiwari, G. Nelson Mathews Mathuram, Laurent Boniface Bembeleza, Joyce Philemon Laiser, Winifrida Julius Luhwago, Theresia Pastory Maduka, John Olais Mollel, Neema Gadiely Mollel, Adella Aloys Mugizi, Isaac Lwaga Mwamakula, Raymond Edwin Rweyemamu, Upendo Firimini Samweli, James Isaac Simpito, Kelvin Ewald Shirima, Anand Anbalagan, Suresh Kumar Arumugam, Vinitha Dhanapal, Kanimozhi Gunasekaran, Neelu Kashyap, Dheeraj Kumar, Durgesh Pandey, Poonam Pandey, Arunkumar Panneerselvam, Sonam Rai, Porselvi Rajendran, Santhoshkumar Sekar, Oliazhagan Sivalingam, Prahalad Soni, Pushpkala Soni, Tara C. Thiagarajan |
License |
CC0 |
Citation / DOI |
|
Source links |
OpenNeuro | NeMAR | Source URL |
Copy-paste BibTeX
@dataset{ds007358,
title = {A subset of large-scale EEG dataset (India + Tanzania)},
author = {John Mary Vianney and Shailender Swaminathan and Jennifer Jane Newson and Dhanya Parameshwaran and Narayan Puthanmadam Subramaniyam and Swaeta Singha Roy and Revocatus Machunda and Achiwa Sapuli and Santanu Pramanik and John Victor Arun Kumar and Pramod Tiwari and G. Nelson Mathews Mathuram and Laurent Boniface Bembeleza and Joyce Philemon Laiser and Winifrida Julius Luhwago and Theresia Pastory Maduka and John Olais Mollel and Neema Gadiely Mollel and Adella Aloys Mugizi and Isaac Lwaga Mwamakula and Raymond Edwin Rweyemamu and Upendo Firimini Samweli and James Isaac Simpito and Kelvin Ewald Shirima and Anand Anbalagan and Suresh Kumar Arumugam and Vinitha Dhanapal and Kanimozhi Gunasekaran and Neelu Kashyap and Dheeraj Kumar and Durgesh Pandey and Poonam Pandey and Arunkumar Panneerselvam and Sonam Rai and Porselvi Rajendran and Santhoshkumar Sekar and Oliazhagan Sivalingam and Prahalad Soni and Pushpkala Soni and Tara C. Thiagarajan},
doi = {10.18112/openneuro.ds007358.v1.0.0},
url = {https://doi.org/10.18112/openneuro.ds007358.v1.0.0},
}
API Reference#
eegdash.datasetEEGDashDatasetDS007358 · Vianney2026eegdash/dataset/registry.py · [source ↗]- class eegdash.dataset.DS007358(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#
A subset of large-scale EEG dataset (India + Tanzania)
- Study:
ds007358(OpenNeuro)- Author (year):
Vianney2026- Canonical:
—
Also importable as:
DS007358,Vianney2026.Modality:
eeg; Experiment type:Resting-state; Subject type:Healthy. Subjects: 2000; recordings: 6000; 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/ds007358 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds007358 DOI: https://doi.org/10.18112/openneuro.ds007358.v1.0.0
Examples
>>> from eegdash.dataset import DS007358 >>> dataset = DS007358(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 ds007358 to reproduce the tutorial on this dataset.
Citation
John Mary Vianney, Shailender Swaminathan, Jennifer Jane Newson, Dhanya Parameshwaran, Narayan Puthanmadam Subramaniyam, … (2019). A subset of large-scale EEG dataset (India + Tanzania). 10.18112/openneuro.ds007358.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.ds007358.v1.0.0.
See Also#
eegdash.dataset.EEGDashDataseteegdash.dataset