{"success":true,"database":"eegdash","data":{"_id":"69d16dff897a7725c66f4c42","dataset_id":"ds004541","associated_paper_doi":null,"authors":["Catalina Saini Ferrón","Gabriela Vargas González","Carlos Valle Araya"],"bids_version":"1.6.0","contact_info":["Carlos Valle Araya"],"contributing_labs":null,"data_processed":false,"dataset_doi":"doi:10.18112/openneuro.ds004541.v1.0.0","datatypes":["eeg","fnirs"],"demographics":{"subjects_count":8,"ages":[],"age_min":null,"age_max":null,"age_mean":null,"species":null,"sex_distribution":null,"handedness_distribution":null},"experimental_modalities":null,"external_links":{"source_url":"https://openneuro.org/datasets/ds004541","osf_url":null,"github_url":null,"paper_url":null},"funding":["Departamento de anestesiología UC"],"ingestion_fingerprint":"220cfa5defeed357ea50cd966216e6d69be9b13200c80389b7c0b3a585c7b8d0","license":"CC0","n_contributing_labs":null,"name":"Multimodal EEG-fNIRS data from patients undergoing general anesthesia","readme":"References\n----------\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\nReferences\n----------\nIn preperation","recording_modality":["eeg","fnirs"],"senior_author":"Carlos Valle Araya","sessions":["01","02"],"size_bytes":3112226120,"source":"openneuro","storage":{"backend":"s3","base":"s3://openneuro.org/ds004541","raw_key":"dataset_description.json","dep_keys":["CHANGES","README","participants.json","participants.tsv"]},"study_design":null,"study_domain":null,"tasks":["anesthesia"],"timestamps":{"digested_at":"2026-04-22T12:26:42.249904+00:00","dataset_created_at":"2023-03-22T16:47:04.065Z","dataset_modified_at":"2026-03-24T19:37:31.000Z"},"total_files":18,"computed_title":"Multimodal EEG-fNIRS data from patients undergoing general anesthesia","nchans_counts":[{"val":59,"count":9},{"val":40,"count":5},{"val":30,"count":3},{"val":38,"count":1}],"sfreq_counts":[{"val":7.8125,"count":9},{"val":1000.0,"count":9}],"stats_computed_at":"2026-04-22T23:16:00.312673+00:00","total_duration_s":43668.023,"tagger_meta":{"config_hash":"3557b68bca409f28","metadata_hash":"8dd10159e489fb2c","model":"openai/gpt-5.2","tagged_at":"2026-04-07T09:32:40.872789+00:00"},"tags":{"pathology":["Surgery"],"modality":["Anesthesia"],"type":["Clinical/Intervention"],"confidence":{"pathology":0.7,"modality":0.8,"type":0.8},"reasoning":{"few_shot_analysis":"No few-shot example is explicitly about anesthesia, but the Parkinson’s Cross-modal Oddball dataset illustrates the convention that when a dataset recruits a clinical group and the primary focus is a clinical condition/biomarker context, the Type is labeled \"Clinical/Intervention\" (rather than e.g., Attention/Perception). Likewise, the Dementia resting-state dataset shows that when recordings are collected in a clinical context (patients with a condition) even if task demands are minimal, the Type can still be \"Clinical/Intervention\" when the purpose is clinical characterization/monitoring. These examples guide labeling this anesthesia dataset as a clinical/monitoring intervention context rather than a basic cognitive construct task.","metadata_analysis":"Key metadata indicating anesthesia/clinical context:\n- Title: \"Multimodal EEG-fNIRS data from patients undergoing general anesthesia\".\n- Tasks: \"anesthesia\".\n- Participants overview: \"Subjects: 8\" (no healthy/control wording; recruited as patients).\nThe readme does not describe a cognitive task paradigm; it mostly contains formatting citations and says \"In preperation\".","paper_abstract_analysis":"No useful paper information.","evidence_alignment_check":"Pathology:\n- Metadata says: \"patients undergoing general anesthesia\" (title).\n- Few-shot pattern suggests: clinical recruitment contexts map to a clinical Pathology label (e.g., Parkinson’s, Dementia, Epilepsy). Here no specific diagnosis is given; the closest allowed pathology label representing perioperative patient recruitment is \"Surgery\".\n- Alignment: PARTIAL (both indicate clinical population, but exact diagnosis absent). Metadata does not conflict; it is simply nonspecific.\n\nModality:\n- Metadata says: \"general anesthesia\" (title) and task \"anesthesia\".\n- Few-shot pattern suggests: choose the stimulus/condition modality category that best matches the dominant experimental condition (e.g., \"Sleep\" for sleep recordings, \"Resting State\" for eyes-open/closed). Here the dominant condition is anesthesia.\n- Alignment: ALIGN (both point to anesthesia as the dominant condition).\n\nType:\n- Metadata says: dataset is from \"patients undergoing general anesthesia\" and task is \"anesthesia\" (no cognitive paradigm described).\n- Few-shot pattern suggests: when the recording is primarily about a clinical state/condition or intervention (Parkinson’s clinical study; Dementia clinical dataset), label Type as \"Clinical/Intervention\".\n- Alignment: ALIGN (anesthesia is a medical intervention/state; no evidence for a primary cognitive construct task).","decision_summary":"Top-2 candidate labels and final selections:\n\n1) Pathology\n- Candidate A: \"Surgery\" (FINAL)\n  Evidence: \"patients undergoing general anesthesia\" (title) implies perioperative/medical-procedure recruitment rather than a normative sample; anesthesia is typically administered in surgical/procedural contexts.\n- Candidate B: \"Other\"\n  Evidence: no explicit underlying diagnosis is provided; could be mixed indications not captured by allowed labels.\n- Head-to-head: \"Surgery\" is more specific to the recruitment context described (general anesthesia patients) than the catch-all \"Other\".\n- Confidence basis: only 1 direct clinical-population quote (title) and no diagnosis details.\n\n2) Modality\n- Candidate A: \"Anesthesia\" (FINAL)\n  Evidence: \"general anesthesia\" (title); task listed as \"anesthesia\".\n- Candidate B: \"Resting State\"\n  Evidence: no explicit sensory stimulation paradigm is described; anesthesia recordings can resemble passive/rest-like recordings.\n- Head-to-head: explicit anesthesia wording outweighs any rest-like inference.\n- Confidence basis: 2 explicit metadata fields (title + tasks) directly naming anesthesia.\n\n3) Type\n- Candidate A: \"Clinical/Intervention\" (FINAL)\n  Evidence: clinical recruitment as \"patients\" plus explicit intervention/state: \"general anesthesia\" (title) and task \"anesthesia\".\n- Candidate B: \"Other\"\n  Evidence: no further description of the scientific aim (e.g., consciousness monitoring vs physiology) is provided.\n- Head-to-head: anesthesia strongly implies an intervention/clinical monitoring purpose rather than a basic cognitive domain.\n- Confidence basis: 2 explicit metadata indicators of an intervention context (title + tasks), but limited details on study aims."}},"canonical_name":null,"name_confidence":0.55,"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":"Ferron2023"}}