DS007358: eeg dataset, 2000 subjects#

Name: A subset of large-scale EEG dataset (India + Tanzania)
Published: 2026
License: CC0

A subset of large-scale EEG dataset (India + Tanzania)

Access recordings and metadata through EEGDash.

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 (2026). A subset of large-scale EEG dataset (India + Tanzania). 10.18112/openneuro.ds007358.v1.0.0

Modality: eeg Subjects: 2000 Recordings: 6000 License: CC0 Source: openneuro

Metadata: Complete (100%)

Quickstart#

Get Started

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)

Query & Filter

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'])

Cite This Dataset

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.

References

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

Dataset Information#

Dataset ID	`DS007358`
Title	A subset of large-scale EEG dataset (India + Tanzania)
Author (year)	`Vianney2026`
Canonical	`Vianney2025`
Importable as	`DS007358`, `Vianney2026`, `Vianney2025`
Year	2026
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	doi:10.18112/openneuro.ds007358.v1.0.0
Source links	OpenNeuro \| NeMAR \| Source URL

Found an issue with this dataset?

If you encounter any problems with this dataset (missing files, incorrect metadata, loading errors, etc.), please let us know!

Report an Issue on GitHub

Technical Details#

Subjects & recordings

Subjects: 2000
Recordings: 6000
Tasks: 3

Channels & sampling rate

Channels: 62 (2408), 60 (833), 74 (811), 72 (770), 68 (707), 50 (216), 66 (150), 56 (63), 48 (29), 44 (6), 54 (4), 65 (3)
Sampling rate (Hz): 128.0 (5733), 256.0 (267)
Duration (hours): 276.1350466579861

API Reference#

Use the DS007358 class to access this dataset programmatically.

class eegdash.dataset.DS007358(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#

Bases: EEGDashDataset

A subset of large-scale EEG dataset (India + Tanzania)

Study:: ds007358 (OpenNeuro)
Author (year):: Vianney2026
Canonical:: Vianney2025

Also importable as: DS007358, Vianney2026, Vianney2025.

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

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/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, overwrite=False)[source]#

Save the dataset to disk.

Parameters:

path (str or Path) – Destination file path.
overwrite (bool, default False) – If True, overwrite existing file.

Return type:

None