{"success":true,"database":"eegdash","data":{"_id":"6953f4239276ef1ee07a32b1","dataset_id":"ds002791","associated_paper_doi":"10.1038/s41597-021-00821-1","authors":[" Ahmad Mheich","Olivier Dufor","Sahar Yassine","Aya Kabbara","Fabrice Wendling","Mahmoud Hassan"],"bids_version":"2.1","contact_info":null,"contributing_labs":null,"data_processed":false,"dataset_doi":"10.18112/openneuro.ds002791.v1.0.0","datatypes":["eeg"],"demographics":{"subjects_count":23,"ages":[23,31,19,23,24,30,19,22,34,19,26,21,20,33,20,30,40,27,24,39,33,25,23],"age_min":19,"age_max":40,"age_mean":26.304347826086957,"species":null,"sex_distribution":{"f":12,"m":11},"handedness_distribution":null},"experimental_modalities":null,"external_links":{"paper_url":"https://www.nature.com/articles/s41597-021-00821-1.pdf"},"funding":[],"ingestion_fingerprint":"bf7c299dff432543cd6191da458e264bb9aa9dc18fb7e09ba2ee4c998536d4b8","license":"CC0","n_contributing_labs":null,"name":"DataSet1","readme":null,"recording_modality":["eeg"],"senior_author":null,"sessions":["naming","spelling"],"size_bytes":50562812738,"source":"openneuro","study_design":null,"study_domain":null,"tasks":[],"timestamps":{"digested_at":"2026-05-31T16:12:17.436783+00:00","dataset_created_at":null,"dataset_modified_at":null},"total_files":92,"storage":{"backend":"s3","base":"s3://openneuro.org/ds002791","raw_key":"dataset_description.json","dep_keys":["CHANGES","participants.tsv"]},"tagger_meta":{"model":"openai/gpt-4o","tagged_at":"2026-06-10T08:19:41Z","source":"eegdash-llm-tagger"},"tags":{"pathology":["Healthy"],"modality":["Unknown"],"type":["Unknown"],"confidence":{"pathology":0.8,"modality":0.6,"type":0.6},"reasoning":{"few_shot_analysis":"The dataset provided has limited information in comparison to the few-shot examples. However, one can leverage the structural insights and focus on participant and task context deduction, which is primarily used to infer labels. This approach is similar to the examples like 'EEG: Three armed bandit gambling task', where college students performed a task but lacked explicit details on the condition and target cohort, thus labeled as Healthy.","metadata_analysis":"The dataset provides authorship and participant details but lacks specific task description or cohort targeting any clinical pathology. The absence of any pathological description or distinct task paradigms indicates a leaning towards 'Healthy'. Key quote: 'age: [\"19\", \"20\", \"21\", \"22\", \"23\", \"24\", \"25\", \"26\", \"27\", \"30\" (and 5 more)]'.","paper_abstract_analysis":"No paper abstract provided to derive additional context or specifics about the project aim or experimental design. The lack of such reinforces reliance on the present metadata.","evidence_alignment_check":"1. Metadata SAYS: General participant ages with no disorder specified, suggesting a norm-centric participant group. 2. Few-shot pattern SUGGESTS: Healthy label is applicable when no specific condition is targeted (consistent with examples like the EEG Motor Movement dataset). 3. They ALIGN since no pathology is highlighted. A clinical purpose wasn't noted either to steer towards 'Clinical' or other specificity seen in exemplars.","decision_summary":"Given the moderate information and clear pattern from examples where participant demography was the major guide without pathology or task specification, HEALTHY is the logical disability label. Supported by metadata: 1. Pathology shows no explicit disorder; hence 'Healthy'. 2. No sensory modality data, assigning: 'Unknown'. 3. Lacking task clarity or precise experimental pursuits leads to 'Unknown' for the type. Confidence: Pathology: 0.8 due to standard foundational presumptions, Modality: 0.6 reflecting deduction in the unknown, and Type: 0.6 for inferred absence of evidence."}},"nemar_citation_count":0,"computed_title":"DataSet1","nchans_counts":[{"val":256,"count":80},{"val":257,"count":12}],"sfreq_counts":[{"val":1000.0,"count":92}],"stats_computed_at":"2026-05-31T19:34:32.517296+00:00","source_url":"https://openneuro.org/datasets/ds002791","total_duration_s":48729.49,"canonical_name":null,"name_confidence":0.64,"name_meta":{"suggested_at":"2026-04-14T10:18:35.342Z","model":"openai/gpt-5.2 + openai/gpt-5.4-mini + deterministic_fallback"},"name_source":"author_year","author_year":"Mheich2020_DataSet1","bad_channels_info":null,"associated_paper_meta":{"channel":"search","confidence":"high","author_overlap":6,"is_oa":true,"oa_status":"gold","source":"paper_resolver"}}}