{"success":true,"database":"eegdash","data":{"_id":"6953f4249276ef1ee07a3408","dataset_id":"ds005624","associated_paper_doi":"10.1101/2022.06.13.496029","authors":["[Unspecified]"],"bids_version":"1.7.0","contact_info":null,"contributing_labs":null,"data_processed":false,"dataset_doi":"doi:10.18112/openneuro.ds005624.v1.0.0","datatypes":["ieeg"],"demographics":{"subjects_count":24,"ages":[],"age_min":null,"age_max":null,"age_mean":null,"species":null,"sex_distribution":null,"handedness_distribution":null},"experimental_modalities":null,"external_links":{"paper_url":"https://doi.org/10.1101/2022.06.13.496029"},"funding":[],"ingestion_fingerprint":"447db81373c696ff8a0eaf7ce4b10b15aba8b7a2f6bb2a73de9d645f9494bb60","license":"CC0","n_contributing_labs":null,"name":"Color Change Detection Task","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\nHoldgraf, C., Appelhoff, S., Bickel, S., Bouchard, K., D'Ambrosio, S., David, O., … Hermes, D. (2019). iEEG-BIDS, extending the Brain Imaging Data Structure specification to human intracranial electrophysiology. Scientific Data, 6, 102. https://doi.org/10.1038/s41597-019-0105-7","recording_modality":["ieeg"],"senior_author":null,"sessions":["0","1","2","3"],"size_bytes":14768554007,"source":"openneuro","study_design":null,"study_domain":null,"tasks":["CCDT"],"timestamps":{"digested_at":"2026-05-31T16:21:22.573351+00:00","dataset_created_at":null,"dataset_modified_at":null},"total_files":35,"storage":{"backend":"s3","base":"s3://openneuro.org/ds005624","raw_key":"dataset_description.json","dep_keys":["CHANGES","README","participants.json","participants.tsv"]},"tagger_meta":{"model":"openai/gpt-4o","tagged_at":"2026-06-10T08:19:41Z","source":"eegdash-llm-tagger"},"tags":{"pathology":["Healthy"],"modality":["Visual"],"type":["Perception"],"confidence":{"pathology":0.8,"modality":0.8,"type":0.8},"reasoning":{"few_shot_analysis":"None of the few-shot examples directly match the dataset task of a 'Color Change Detection Task', but we have examples like 'Meta-rdk: Preprocessed EEG data' as it involves visual perception tasks in healthy participants and patient group with schizophrenia. It provides a prototypical example for assigning the 'Visual' modality and 'Perception' type when a visual stimulus is used with a task focusing on detecting changes or perceptual discrimination.","metadata_analysis":"The title 'Color Change Detection Task' suggests a visual task. The lack of explicit pathology or clinical population description in the dataset details implies that the participants are likely healthy individuals. No specific participant details suggest that there are no clinical or special populations involved.","paper_abstract_analysis":"No useful paper information.","evidence_alignment_check":"1. Pathology: The metadata does not specify any clinical condition or special population ('[Unspecified]'). With no specific pathology mentioned, 'Healthy' is the most logical assumption. Few-shot suggests what to do in absence of pathology information.\n2. Modality: The title implies visual modality ('Color Change Detection Task'). The few-shot example 'Meta-rdk: Preprocessed EEG data' with 'Visual' as modality supports this selection.\n3. Type: The task appears focused on detecting changes in visual stimuli, aligning with perceptual tasks. This reasoning aligns with the few-shot 'Meta-rdk' with 'Perception'. No conflicting information is present.","decision_summary":"For pathology, 'Healthy' is the most suitable label due to lack of any specified condition, similar to assumptions in few-shot where no pathology is provided. Modality is 'Visual' since the task is about color detection, consistent with few-shot example approaches. Type 'Perception' is selected because the task involves detecting visual changes, a typical perception study focus in visual tasks. Confidence is derived from strong alignment of task title clues with few-shot understanding."}},"nemar_citation_count":0,"computed_title":"Color Change Detection Task","nchans_counts":[{"val":74,"count":4},{"val":172,"count":3},{"val":223,"count":3},{"val":162,"count":2},{"val":152,"count":2},{"val":151,"count":2},{"val":95,"count":2},{"val":100,"count":2},{"val":115,"count":2},{"val":111,"count":2},{"val":127,"count":1},{"val":123,"count":1},{"val":128,"count":1},{"val":118,"count":1},{"val":191,"count":1},{"val":101,"count":1},{"val":173,"count":1},{"val":228,"count":1},{"val":189,"count":1},{"val":166,"count":1},{"val":205,"count":1}],"sfreq_counts":[{"val":512.0,"count":26},{"val":1024.0,"count":9}],"stats_computed_at":"2026-05-31T19:34:32.601649+00:00","total_duration_s":41331.974609375,"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":"DS5624_ColorChangeDetection","bad_channels_info":null,"associated_paper_meta":{"channel":"openneuro/associatedPaperDOI","confidence":"high","author_overlap":-1,"is_oa":true,"oa_status":"preprint","source":"paper_resolver"}}}