{"success":true,"database":"eegdash","data":{"_id":"6953f4249276ef1ee07a343b","dataset_id":"ds006159","associated_paper_doi":null,"authors":["Mateo Leganes-Fonteneau"],"bids_version":"1.7.0","contact_info":null,"contributing_labs":null,"data_processed":true,"dataset_doi":"doi:10.18112/openneuro.ds006159.v1.0.0","datatypes":["eeg"],"demographics":{"subjects_count":61,"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/ds006159","osf_url":null,"github_url":null,"paper_url":null},"funding":[],"ingestion_fingerprint":"20fe5259c661819e7be98e7318e34eaddc4c26d3ee0961eba15e33fb609c6da3","license":"CC0","n_contributing_labs":null,"name":"Implicit Learning EEG (BioSemi)","readme":null,"recording_modality":["eeg"],"senior_author":null,"sessions":[],"size_bytes":122876963516,"source":"openneuro","study_design":null,"study_domain":null,"tasks":["implicitlearn"],"timestamps":{"digested_at":"2026-05-31T16:23:38.689635+00:00","dataset_created_at":null,"dataset_modified_at":null},"total_files":61,"storage":{"backend":"s3","base":"s3://openneuro.org/ds006159","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":["Learning"],"confidence":{"pathology":0.7,"modality":0.6,"type":0.8},"reasoning":{"few_shot_analysis":"The few-shot example labeled 'Healthy', 'Visual', and 'Learning' was a 'Probabilistic Learning with Affective Feedback' study. It involved healthy participants in a learning task with visual stimuli. This aligns with the typical profile of implicit learning studies which often involve understanding patterns or sequences, and the EEG dataset titled 'Implicit Learning EEG (BioSemi)' likely fits this task profile given the title's emphasis on learning. Thus, the few-shot example suggests a similar labeling convention.","metadata_analysis":"The dataset is titled 'Implicit Learning EEG (BioSemi)', which implies a focus on learning via EEG recorded with a BioSemi device. There is no explicit mention of clinical populations, stimuli modalities, or cognitive constructs beyond implicit learning, but the title suggests healthy subjects engaging in a learning-related task.","paper_abstract_analysis":"No useful paper information is provided to further clarify the specifics of the dataset.","evidence_alignment_check":"1. Pathology: Metadata SAYS nothing about clinical populations, few-shot suggests 'Healthy' based on typical implicit learning contexts. ALIGN. 2. Modality: Metadata SAYS nothing about sensory channels, few-shot example used 'Visual' for learning tasks, suggesting perhaps visual stimuli. PUZZLE, no explicit alignment. 3. Type: Metadata SAYS 'Learning', few-shot suggests 'Learning' type for tasks focusing on implicit or probabilistic learning. ALIGN.","decision_summary":"Top-2 candidates for pathology are 'Healthy' and 'Unknown'. Given the learning task context and lack of clinical mention, 'Healthy' is selected with confidence from the few-shot inference. Top-2 for modality are 'Visual' and 'Unknown'. Without explicit information, 'Visual' is cautiously selected based on few-shot alignment. Top-2 for type are 'Learning' and 'Unknown'. The title directly implies 'Learning', so it is selected with high confidence. No metadata conflicts arose that necessitated overriding few-shot guidance."}},"computed_title":"Implicit Learning EEG (BioSemi)","nchans_counts":[{"val":73,"count":14}],"sfreq_counts":[{"val":1024.0,"count":14}],"stats_computed_at":"2026-05-31T19:34:32.602300+00:00","total_duration_s":51477.0,"canonical_name":null,"name_confidence":0.34,"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":"LeganesFonteneau2025","bad_channels_info":null}}