{"success":true,"database":"eegdash","data":{"_id":"6953f4239276ef1ee07a3293","dataset_id":"ds000247","associated_paper_doi":null,"authors":["Guiomar Niso","Jeremy Moreau","Elizabeth Bock","Francois Tadel","Sylvain Baillet"],"bids_version":"1.0.2","contact_info":["Julia Guiomar Niso Galán"],"contributing_labs":null,"data_processed":true,"dataset_doi":"doi:10.18112/openneuro.ds000247.v1.0.2","datatypes":["meg"],"demographics":{"subjects_count":6,"ages":[21,30,28,35,23],"age_min":21,"age_max":35,"age_mean":27.4,"species":null,"sex_distribution":{"m":3,"f":2},"handedness_distribution":null},"experimental_modalities":null,"external_links":{"source_url":"https://openneuro.org/datasets/ds000247","osf_url":null,"github_url":null,"paper_url":null},"funding":["Quebec Bioimaging Network Strategic Initiative (QBIN 5886)"],"ingestion_fingerprint":"da4a26c14400e22debabbf6cf98b1e793c338905cc87db89ff4b419b65c14d30","license":"CC0","n_contributing_labs":null,"name":"MEG-BIDS OMEGA RestingState_sample","readme":"# OMEGA - Resting State Sample Dataset\n## License\n- This dataset was obtained from **The Open MEG Archive** (OMEGA, https://omega.bic.mni.mcgill.ca).\n- You are free to use all data in OMEGA for research purposes; please acknowledge its authors and cite the following reference in your publications if you have used data from OMEGA:\n- Niso G., Rogers C., Moreau J.T., Chen L.Y., Madjar C., Das S., Bock E., Tadel F., Evans A.C., Jolicoeur P.,  Baillet S. (2016). OMEGA: The Open MEG Archive. NeuroImage 124, 1182-1187. doi:  https://doi.org/10.1016/j.neuroimage.2015.04.028. OMEGA is available at: https://omega.bic.mni.mcgill.ca\n## Description\n**Experiment**\n- 5 subjects x 5 minute resting sessions, eyes open\n**MEG acquisition**\n- Recorded at the Montreal Neurological Institute in 2012-2016\n- Acquisition with CTF 275 MEG system at 2400Hz sampling rate\n- Anti-aliasing low-pass filter at 600Hz, files may be saved with or without the CTF 3rd order gradient compensation\n- Recorded channels (at least 297), include:\n  * 26 MEG reference sensors (#2-#27)\n  * 270 MEG axial gradiometers (#28-#297)\n  * 1 ECG bipolar (EEG057/#298) - Not available in the empty room recordings\n  * 1 vertical EOG bipolar (EEG058/#299) - Not available in the empty room recordings\n  * 1 horizontal EOG bipolar (EEG059/#300) - Not available in the empty room recordings\n**Head shape and fiducial points**\n- 3D digitization using a Polhemus Fastrak device driven by Brainstorm. The .pos files contain:\n  * The center of the CTF coils\n  * The anatomical references we use in Brainstorm: nasion and ears as illustrated here\n  * Around 100 head points distributed on the hard parts of the head (no soft tissues).\n**Subject anatomy**\n- Structural T1 image (defaced for anonymization purposes)\n- Processed with FreeSurfer 5.3\n- The anatomical fiducials (NAS, LPA, RPA) have already been marked and saved in the files fiducials.m\n**BIDS**\n- The data in this dataset has been organized according to the MEG-BIDS specification (Brain Imaging Data Structure, http://bids.neuroimaging.io) (Niso et al. 2018)\n- Niso 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.M., Tadel F., Wexler J., Baillet S. (2018). MEG-BIDS: an extension to the Brain Imaging Data Structure for magnetoencephalography. Scientific Data; 5, 180110. https://doi.org/10.1038/sdata.2018.110\n**Release history:**\n- 2016-12-01: initial release\n- 2018-07-18: release OpenNeuro ds000247 (00001 and 00002)","recording_modality":["meg"],"senior_author":"Sylvain Baillet","sessions":["01","18901014","18901015","18910228","18910303","18910512"],"size_bytes":11024087953,"source":"openneuro","study_design":null,"study_domain":null,"tasks":["noise","rest"],"timestamps":{"digested_at":"2026-04-06T13:03:47.393422+00:00","dataset_created_at":"2018-03-30T18:40:38.407Z","dataset_modified_at":"2024-04-24T10:50:08.000Z"},"total_files":10,"storage":{"backend":"s3","base":"s3://openneuro.org/ds000247","raw_key":"dataset_description.json","dep_keys":["CHANGES","README","participants.json","participants.tsv"]},"tagger_meta":{"config_hash":"4a051be509a0e3d0","metadata_hash":"43ec2f840397d31a","model":"openai/gpt-5.2","tagged_at":"2026-01-20T10:08:45.923494+00:00"},"tags":{"pathology":["Healthy"],"modality":["Resting State"],"type":["Resting-state"],"confidence":{"pathology":0.7,"modality":0.85,"type":0.85},"reasoning":{"few_shot_analysis":"Closest convention match is the few-shot example \"A Resting-state EEG Dataset for Sleep Deprivation\" labeled as Pathology=Healthy, Modality=Resting State, Type=Resting-state, because it is explicitly a resting-state recording (no external task demands). Another relevant convention match is the dementia resting-state dataset, which shows that when a clinical diagnosis is explicitly stated, Pathology becomes the diagnosis and Type often becomes Clinical/Intervention; in the current dataset no diagnosis is stated, so we follow the healthy resting-state convention.","metadata_analysis":"Key task/population facts from the dataset README: (1) Rest condition is explicit: \"5 subjects x 5 minute resting sessions, eyes open\". (2) No task/stimulus is described beyond resting: the description is framed as \"OMEGA - Resting State Sample Dataset\" and provides acquisition/anatomy details but no experimental paradigm/stimuli. (3) No clinical recruitment is mentioned anywhere; only acquisition context: \"Recorded at the Montreal Neurological Institute in 2012-2016\".","paper_abstract_analysis":"No useful paper information.","evidence_alignment_check":"Pathology: Metadata says there is no patient group mentioned (e.g., only \"5 subjects\" and acquisition details; no diagnosis terms). Few-shot pattern suggests using Healthy when no clinical recruitment is indicated (as in the resting-state sleep deprivation example). ALIGN.\nModality: Metadata says \"resting sessions, eyes open\" (no sensory stimulus). Few-shot pattern suggests labeling such datasets as \"Resting State\" modality. ALIGN.\nType: Metadata says \"Resting State Sample Dataset\" and \"resting sessions\" (no cognitive task). Few-shot pattern suggests \"Resting-state\" type for eyes-open/eyes-closed rest recordings. ALIGN.","decision_summary":"Pathology top-2: (1) Healthy — supported by absence of any clinical recruitment language and generic \"5 subjects\" sample; (2) Unknown — possible if participant health status were unspecified, but few-shot conventions use Healthy for normative cohorts when no disorder focus is stated. Winner: Healthy. Alignment: aligns with few-shot resting-state healthy convention. Confidence evidence: quotes include \"5 subjects x 5 minute resting sessions\" and no diagnosis mention.\nModality top-2: (1) Resting State — supported by \"resting sessions, eyes open\" and dataset title \"Resting State\"; (2) Unknown — if one insisted modality requires explicit stimulus modality, but rest is explicitly stated. Winner: Resting State. Confidence evidence: explicit resting-state quotes.\nType top-2: (1) Resting-state — supported by \"Resting State Sample Dataset\" and \"resting sessions\"; (2) Other — if it were mainly a methods/acquisition dataset, but the experimental condition is clearly rest. Winner: Resting-state. Confidence evidence: explicit resting-state quotes."}},"nemar_citation_count":3,"computed_title":"MEG-BIDS OMEGA RestingState_sample","nchans_counts":[{"val":297,"count":5},{"val":330,"count":3},{"val":300,"count":2}],"sfreq_counts":[{"val":2400.0,"count":10}],"stats_computed_at":"2026-04-04T21:29:34.872915+00:00","total_duration_s":3657.0,"author_year":"Niso2018","canonical_name":["OMEGA"],"name_source":"canonical"}}