{"success":true,"database":"eegdash","data":{"_id":"6953f4239276ef1ee07a32b7","dataset_id":"ds002908","associated_paper_doi":"10.1523/jneurosci.0572-21.2022","authors":["Rafal Bogacz","Vladimir Litvak"],"bids_version":"1.0.1","contact_info":null,"contributing_labs":null,"data_processed":false,"dataset_doi":"10.18112/openneuro.ds002908.v1.0.0","datatypes":["meg"],"demographics":{"subjects_count":13,"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://www.jneurosci.org/content/jneuro/42/23/4681.full.pdf"},"funding":["MC_UU_12024/5","MC_UU_00003/1","BB/S006338/1","203147/Z/16/Z","MR/K005464/1"],"ingestion_fingerprint":"8fc17b60bf67ffd6a1de8158a752ccd72c8397071456c8a5d22e74cb5ad2e6ca","license":"CC0","n_contributing_labs":null,"name":"Human MEG recordings during sequential conflict task","readme":null,"recording_modality":["meg"],"senior_author":null,"sessions":["1","2","3","4","5","6"],"size_bytes":62574213453,"source":"openneuro","study_design":null,"study_domain":null,"tasks":["mouse"],"timestamps":{"digested_at":"2026-05-31T16:12:25.029487+00:00","dataset_created_at":null,"dataset_modified_at":null},"total_files":53,"storage":{"backend":"s3","base":"s3://openneuro.org/ds002908","raw_key":"dataset_description.json","dep_keys":["CHANGES"]},"tagger_meta":{"model":"openai/gpt-4o","tagged_at":"2026-06-10T08:19:41Z","source":"eegdash-llm-tagger"},"tags":{"pathology":["Healthy"],"modality":["Unknown"],"type":["Decision-making"],"confidence":{"pathology":0.8,"modality":0.5,"type":0.8},"reasoning":{"few_shot_analysis":"The few-shot examples provide a mix of clinical populations participating in tasks involving conflict, decision-making, and visual modalities. One similar example is the dataset titled 'EEG: Reinforcement Learning in Parkinson's', which involves Parkinson's patients conducting a decision-making task indicating that similar pathological classifications would follow a pattern where the task paradigm involved detects cognitive function and enhances decision-making. However, the metadata here does not specify a clinical condition or a specific modality for sensory input, making the convention of using 'Healthy' as the pathology for unlabeled populations the common pattern.","metadata_analysis":"The dataset title 'Human MEG recordings during sequential conflict task' suggests that the research involves decision-making or conflict detection in tasks. The description mentions the involvement of authors in a study related to conflict detection in decision tasks. However, the metadata does not provide details about pathology-related recruitment or specific stimulus modality beyond the fact that the study seems to assess decision-making during such tasks ('Conflict detection in a sequential decision task').","paper_abstract_analysis":"No useful paper information.","evidence_alignment_check":"Pathology: The metadata does not explicitly indicate a clinical population, so by default, it suggests 'Healthy' which aligns with the common few-shot examples without pathology-specific recruitment. Modality: The metadata lacks specific mention of sensory input channels, not aligning with any specific modality evidenced in few-shot examples; thus could default to 'Unknown'. Type: Conflict detection is emphasized, aligning with 'Decision-making' as demonstrated in decision-based tasks within few-shot examples.","decision_summary":"Pathology: The metadata suggests 'Healthy', due to absence of specific clinical recruitment, aligning with few-shot examples where no particular pathology is involved (Confidence: 0.8). Modality: Lacks sufficient evidence to specify sensory channels involved, resulting in 'Unknown' as balancing lack of explicit detailing (Confidence: 0.5). Type: Strong alignment with 'Decision-making' as suggested by conflict detection task (Confidence: 0.8)."}},"nemar_citation_count":1,"computed_title":"Human MEG recordings during sequential conflict task","nchans_counts":[{"val":299,"count":53}],"sfreq_counts":[{"val":2400.0,"count":53}],"stats_computed_at":"2026-05-31T19:34:32.517418+00:00","total_duration_s":18381.0,"canonical_name":null,"name_confidence":0.83,"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":"Bogacz2020","bad_channels_info":null,"acknowledgements":"We thank Dr. Ashwini Oswal, Dr. Simon Little, Dr. David Pedrosa, Dr. Damian Herz and Dr.Viswas Dayal for clinical support of patient recordings, as well as the patients and their families.","ethics_approvals":["University College London Ethics Committee approval for minimum risk magnetoencephalography studies of healthy human cognition"],"how_to_acknowledge":"Please cite this paper: Patai, Z. E., Foltynie, T., Limousin, P., Hariz, M. I., Zrinzo, L.,Bogacz, R., Litvak, V. (2020). Conflict detection in a sequential decision task is associated with increased cortico-subthalamic coherence and prolonged subthalamic oscillatory response in the beta band.","references_and_links":[""],"associated_paper_meta":{"channel":"search","confidence":"high","author_overlap":2,"is_oa":true,"oa_status":"bronze","source":"paper_resolver"}}}