{"success":true,"database":"eegdash","data":{"_id":"6953f4249276ef1ee07a3339","dataset_id":"ds004346","associated_paper_doi":"10.1016/j.neuroimage.2022.119047","authors":["Oscar Ferrante","Ling Liu","Tamas Minarik","Urszula Gorska","Tara Ghafari","Huan Luo","Ole Jensen"],"bids_version":"1.7.0","contact_info":null,"contributing_labs":null,"data_processed":true,"dataset_doi":"doi:10.18112/openneuro.ds004346.v1.0.8","datatypes":["meg"],"demographics":{"subjects_count":1,"ages":[44],"age_min":44,"age_max":44,"age_mean":44.0,"species":null,"sex_distribution":null,"handedness_distribution":null},"experimental_modalities":null,"external_links":{"paper_url":"https://doi.org/10.1016/j.neuroimage.2022.119047"},"funding":[],"ingestion_fingerprint":"5c83d1ffa183df6069ab7bfa903252ce52198d1ae9031250be35ca70c8694f9f","license":"CC0","n_contributing_labs":null,"name":"FLUX: A pipeline for MEG analysis","readme":"References\n----------\nFerrante, O., Liu, L., Minarik, T., Gorska, U., Ghafari, T., Luo, H., & Jensen, O. (2022). FLUX: A pipeline for MEG analysis. NeuroImage, 253, 119047. https://doi.org/10.1016/j.neuroimage.2022.119047\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\nNiso, G., Gorgolewski, K. J., Bock, E., Brooks, T. L., Flandin, G., Gramfort, A., Henson, R. N., Jas, M., Litvak, V., Moreau, J., Oostenveld, R., Schoffelen, J., Tadel, F., Wexler, J., Baillet, S. (2018). MEG-BIDS, the brain imaging data structure extended to magnetoencephalography. Scientific Data, 5, 180110. https://doi.org/10.1038/sdata.2018.110","recording_modality":["meg"],"senior_author":null,"sessions":["01"],"size_bytes":18062834948,"source":"openneuro","study_design":null,"study_domain":null,"tasks":["SpAtt"],"timestamps":{"digested_at":"2026-05-31T16:14:29.390768+00:00","dataset_created_at":null,"dataset_modified_at":null},"total_files":3,"storage":{"backend":"s3","base":"s3://openneuro.org/ds004346","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":["Decision-making"],"confidence":{"pathology":0.7,"modality":0.8,"type":0.8},"reasoning":{"few_shot_analysis":"A few-shot example involved an auditory oddball task used with TBI patients, labeled as 'Auditory' for 'Modality' and 'Decision-making' for 'Type'. The paradigm for this dataset supports these labels for decision-making tasks with auditory stimuli. Additionally, there are examples with visual decision-making tasks, such as those involving Parkinson's patients, which also guide appropriately labeling tasks involving decisions based on sensory input.","metadata_analysis":"The dataset title is 'FLUX: A pipeline for MEG analysis'. The metadata includes events like 'cue_left', 'cue_right', 'target_onset', and 'left_resp_maybe', 'right_resp_maybe', suggesting a decision-making task based on responses to presented cues, typical of perceptual decision-making paradigms.","paper_abstract_analysis":"No specific information about a task paradigm or participant population is provided in the general dataset references. The references focus on data structure optimizations rather than study specifics.","evidence_alignment_check":"1. Pathology: The metadata does not mention a specific clinical population. It lacks details on participant recruitment strategy concerning health conditions. Aligns with few-shot Healthy datasets. 2. Modality: Although not directly stated, the use of 'cue_left' and 'cue_right' events suggests visual modality, aligning with visual decision-making target response tasks seen in examples. 3. Type: The presence of 'cue', 'response', and 'target_onset' clearly marks a focus on decision-making processes, be it perceptual or cognitive, consistent with examples where decision-making is labeled under such paradigms.","decision_summary":"Pathology: Both 'Healthy' and 'Other' were considered, but given the lack of specific pathology and alignment with examples that describe generic healthy cohort studies, 'Healthy' was selected with a confidence of 0.7. Modality: 'Visual' is inferred from cue and target events without explicit mention of another sensory modality, gaining confidence from event description, confidence 0.8. Type: 'Decision-making' fits the trial-response format indicated by task events, similar to few-show paradigms, confidence 0.8."}},"nemar_citation_count":0,"computed_title":"FLUX: A pipeline for MEG analysis","nchans_counts":[{"val":343,"count":2}],"sfreq_counts":[{"val":1000.0,"count":2}],"stats_computed_at":"2026-05-31T19:34:32.598847+00:00","total_duration_s":2890.998,"canonical_name":null,"name_confidence":0.78,"name_meta":{"suggested_at":"2026-04-14T10:18:35.343Z","model":"openai/gpt-5.2 + openai/gpt-5.4-mini + deterministic_fallback"},"name_source":"canonical","author_year":"Ferrante2022","bad_channels_info":null,"associated_paper_meta":{"channel":"text/readme","confidence":"high","author_overlap":6,"is_oa":true,"oa_status":"gold","source":"paper_resolver"}}}