{"success":true,"database":"eegdash","data":{"_id":"6953f4249276ef1ee07a3408","dataset_id":"ds005624","associated_paper_doi":null,"authors":["[Unspecified]"],"bids_version":"1.7.0","contact_info":["Creence Lin"],"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":{"source_url":"https://openneuro.org/datasets/ds005624","osf_url":null,"github_url":null,"paper_url":null},"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":"[Unspecified]","sessions":["0","1","2","3"],"size_bytes":14768426442,"source":"openneuro","study_design":null,"study_domain":null,"tasks":["CCDT"],"timestamps":{"digested_at":"2026-04-22T12:28:39.670035+00:00","dataset_created_at":"2024-11-07T06:58:53.218Z","dataset_modified_at":"2024-11-07T19:54:19.000Z"},"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":{"config_hash":"3557b68bca409f28","metadata_hash":"28faa783d8a52c87","model":"openai/gpt-5.2","tagged_at":"2026-04-07T09:32:40.872789+00:00"},"tags":{"pathology":["Unknown"],"modality":["Visual"],"type":["Memory"],"confidence":{"pathology":0.4,"modality":0.7,"type":0.6},"reasoning":{"few_shot_analysis":"Closest few-shot by paradigm/modality is the visual discrimination example (Meta-rdk: Preprocessed EEG data), where a clearly visual task (“We used a visual discrimination task…”) maps to Modality=Visual and Type=Perception. This guides the convention that when a task is explicitly visual, Modality should be Visual. However, the current dataset’s paradigm is a “Color Change Detection Task”, which by common experimental convention is often used to probe visual working memory/attentional capacity rather than pure sensory perception; thus the few-shot mainly anchors the Modality mapping, while Type requires additional inference beyond the provided few-shots.","metadata_analysis":"Key available metadata is sparse. Explicit snippets:\n1) Title: \"Color Change Detection Task\" (suggests a visual color-based change detection paradigm).\n2) Tasks: \"CCDT\" (an acronym consistent with Color Change Detection Task).\n3) Participants: \"Subjects: 24\" (no stated diagnosis, age, or recruitment criteria; no mention of patients/controls).","paper_abstract_analysis":"No useful paper information.","evidence_alignment_check":"Pathology:\n- Metadata says: only \"Subjects: 24\" with no clinical descriptors.\n- Few-shot pattern suggests: many cognitive EEG tasks are in healthy volunteers, but this is not a metadata FACT.\n- Alignment: INSUFFICIENT metadata; cannot confirm Healthy. Choose Unknown.\n\nModality:\n- Metadata says: \"Color Change Detection Task\" (color change implies visual stimuli).\n- Few-shot pattern suggests: visual tasks (e.g., visual discrimination) -> Modality=Visual.\n- Alignment: ALIGNS (both point to Visual).\n\nType:\n- Metadata says: only the task name \"Color Change Detection Task\" / \"CCDT\" without further description.\n- Few-shot pattern suggests: visual discrimination tasks often labeled Perception; however change-detection paradigms are commonly used for visual working memory/attention, which is not directly exemplified in the few-shots.\n- Alignment: PARTIAL/UNCERTAIN; task is visual, but cognitive construct (Perception vs Memory vs Attention) is not explicitly stated. Use best-fit inference from task name.","decision_summary":"Top-2 candidates (with head-to-head comparison) and final decisions:\n\nPathology candidates:\n1) Unknown — Evidence: participants only described as \"Subjects: 24\"; no diagnosis/health screening mentioned.\n2) Healthy — Weak inference based on typical non-clinical cognitive EEG tasks, but not explicitly stated.\nDecision: Unknown wins because there is no explicit recruitment/diagnosis information.\n\nModality candidates:\n1) Visual — Evidence: \"Color Change Detection Task\" (color-based change implies visual stimulus).\n2) Unknown — Would apply if task/stimulus channel were not inferable.\nDecision: Visual wins; task name strongly implies visual stimuli.\n\nType candidates:\n1) Memory — Change-detection tasks are commonly designed to test visual short-term/working memory (detecting whether an item’s color changed across a delay).\n2) Attention — Also plausible because change detection can be framed as attentional selection/monitoring.\nDecision: Memory slightly stronger given the canonical use of “color change detection” as a visual working memory paradigm, but confidence is limited because metadata does not describe delays/encoding/maintenance explicitly.\n\nConfidence justification:\n- Pathology: low confidence due to lack of any explicit health/diagnosis statements (only subject count).\n- Modality: moderate confidence from explicit task title indicating color/visual stimuli.\n- Type: moderate-low confidence because construct is inferred from paradigm name without protocol details."}},"nemar_citation_count":0,"computed_title":"Color Change Detection Task","nchans_counts":[{"val":74,"count":4},{"val":223,"count":3},{"val":172,"count":3},{"val":100,"count":2},{"val":95,"count":2},{"val":111,"count":2},{"val":152,"count":2},{"val":162,"count":2},{"val":115,"count":2},{"val":151,"count":2},{"val":205,"count":1},{"val":173,"count":1},{"val":191,"count":1},{"val":101,"count":1},{"val":118,"count":1},{"val":228,"count":1},{"val":166,"count":1},{"val":123,"count":1},{"val":189,"count":1},{"val":127,"count":1},{"val":128,"count":1}],"sfreq_counts":[{"val":512.0,"count":26},{"val":1024.0,"count":9}],"stats_computed_at":"2026-04-22T23:16:00.310699+00:00","total_duration_s":41040.9150390625,"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"}}