{"success":true,"database":"eegdash","data":{"_id":"69d16e04897a7725c66f4c47","dataset_id":"ds007358","associated_paper_doi":null,"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"],"bids_version":"1.8.0","contact_info":["Narayan Puthanmadam Subramaniyam"],"contributing_labs":null,"data_processed":false,"dataset_doi":"doi:10.18112/openneuro.ds007358.v1.0.0","datatypes":["eeg"],"demographics":{"subjects_count":2000,"ages":[],"age_min":null,"age_max":null,"age_mean":null,"species":null,"sex_distribution":{"f":1086,"m":877,"o":2},"handedness_distribution":null},"experimental_modalities":null,"external_links":{"source_url":"https://openneuro.org/datasets/ds007358","osf_url":null,"github_url":null,"paper_url":null},"funding":[],"ingestion_fingerprint":"d398d1549cff8f4bd052909032a9bf533694c027446ee88faceb981e6e40af64","license":"CC0","n_contributing_labs":null,"name":"A subset of large-scale EEG dataset (India + Tanzania)","readme":"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.\nA 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.\nReferences\n----------\nVianney 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).\nNewson 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.\nAppelhoff, 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\nPernet, 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","recording_modality":["eeg"],"senior_author":"Tara C. Thiagarajan","sessions":[],"size_bytes":17264324344,"source":"openneuro","storage":{"backend":"s3","base":"s3://openneuro.org/ds007358","raw_key":"dataset_description.json","dep_keys":["CHANGES","README","participants.json","participants.tsv"]},"study_design":null,"study_domain":null,"tasks":["ec","eo","pc"],"timestamps":{"digested_at":"2026-04-22T12:30:14.299169+00:00","dataset_created_at":"2026-02-04T07:10:36.872Z","dataset_modified_at":"2026-02-04T11:22:29.000Z"},"total_files":6000,"computed_title":"A subset of large-scale EEG dataset (India + Tanzania)","nchans_counts":[{"val":62,"count":2408},{"val":60,"count":833},{"val":74,"count":811},{"val":72,"count":770},{"val":68,"count":707},{"val":50,"count":216},{"val":66,"count":150},{"val":56,"count":63},{"val":48,"count":29},{"val":44,"count":6},{"val":54,"count":4},{"val":65,"count":3}],"sfreq_counts":[{"val":128.0,"count":5733},{"val":256.0,"count":267}],"stats_computed_at":"2026-04-22T23:16:00.312747+00:00","total_duration_s":994086.16796875,"tagger_meta":{"config_hash":"3557b68bca409f28","metadata_hash":"a88c186625bf9186","model":"openai/gpt-5.2","tagged_at":"2026-04-07T09:32:40.872789+00:00"},"tags":{"pathology":["Healthy"],"modality":["Resting State"],"type":["Resting-state"],"confidence":{"pathology":0.8,"modality":0.75,"type":0.8},"reasoning":{"few_shot_analysis":"Most similar few-shot convention is the resting-state dataset labeled Healthy / Resting State / Resting-state (\"A Resting-state EEG Dataset for Sleep Deprivation\"), which explicitly uses eyes-open/eyes-closed resting recordings and is categorized as Resting State modality and Resting-state type. This target dataset’s tasks include \"ec\" and \"eo\", strongly matching the same resting-state eyes-closed/eyes-open structure, so we follow the same labeling convention.","metadata_analysis":"Key metadata indicates large-scale field EEG in a general population sample without clinical recruitment, and includes eyes-closed/eyes-open tasks:\n- \"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.\"\n- \"collecting data from a diverse sample of 7,933 participants ranging from Hadzabe hunter-gatherers to office workers\" (subset uploaded here).\n- Tasks listed: \"tasks\": [\"ec\", \"eo\", \"pc\"], where \"ec\"/\"eo\" are standard shorthand for eyes-closed/eyes-open resting EEG.\nNo explicit disease/diagnosis-based recruitment is described; MHQ is reported as an assessment/metadata variable rather than a clinical cohort definition.","paper_abstract_analysis":"No useful paper information.","evidence_alignment_check":"Pathology:\n- Metadata says: \"diverse sample of 7,933 participants\" and this upload includes \"metadata like age, mental health quotient (MHQ) score\" with no mention of patients/diagnoses.\n- Few-shot pattern suggests: large non-clinical cohorts with resting EEG are labeled Healthy.\n- Alignment: ALIGN (no explicit clinical recruitment; treat as Healthy).\n\nModality:\n- Metadata says: tasks include \"ec\" and \"eo\" (eyes closed/open), consistent with resting EEG recordings.\n- Few-shot pattern suggests: eyes-open/eyes-closed recordings map to Modality = Resting State.\n- Alignment: ALIGN (\"pc\" is unclear, but dominant described tasks include ec/eo resting).\n\nType:\n- Metadata says: dataset is for field EEG at scale and data quality monitoring; no explicit cognitive task paradigm is described, and tasks include eyes-open/closed.\n- Few-shot pattern suggests: passive EC/EO datasets map to Type = Resting-state.\n- Alignment: ALIGN.","decision_summary":"Top-2 candidates and selection:\n\nPathology:\n1) Healthy (selected): supported by (i) \"diverse sample ... hunter-gatherers to office workers\" and (ii) only assessments like \"MHQ score\" mentioned, not clinical diagnoses.\n2) Unknown: possible only because recruitment criteria are not explicitly stated as 'healthy', but absence of any named disorder favors Healthy.\nAlignment status: aligned with few-shot resting-state healthy convention.\nConfidence justification: 2 explicit non-clinical/general-population cues + no conflicting clinical quotes.\n\nModality:\n1) Resting State (selected): supported by tasks \"ec\" and \"eo\" (eyes closed/open resting EEG).\n2) Unknown: because \"pc\" is not defined in provided metadata.\nAlignment status: aligned with few-shot EC/EO resting datasets.\nConfidence justification: explicit task labels ec/eo; minor uncertainty from pc.\n\nType:\n1) Resting-state (selected): supported by EC/EO resting tasks and lack of an active cognitive paradigm description.\n2) Clinical/Intervention: conceivable due to MHQ/mental health outcomes mention, but no intervention/clinical cohort recruitment is described.\nAlignment status: aligned with few-shot resting-state labeling.\nConfidence justification: ec/eo implies resting-state; no competing task evidence."}},"canonical_name":null,"name_confidence":0.66,"name_meta":{"suggested_at":"2026-04-14T10:18:35.343Z","model":"openai/gpt-5.2 + openai/gpt-5.4-mini + deterministic_fallback"},"name_source":"author_year","author_year":"Vianney2026"}}