{"dataset":{"id":"139","dataset_id":"on003645","name":"Face processing MEEG dataset with HED annotation","description":"Imported from OpenNeuro ds003645","owner_user_id":2,"status":"active","github_repo":"nemarDatasets/on003645","concept_doi":"10.82901/nemar.on003645","latest_version_doi":"10.82901/nemar.on003645.v1.0.0","created_at":"2026-03-09 04:19:40","updated_at":"2026-09-15 15:56:44","zenodo_concept_id":null,"is_sandbox":0,"visibility":"public","ezid_status":"public","enrichment_json":"{\n  \"version\": \"2.0\",\n  \"pipeline_stage\": \"enriched\",\n  \"title\": \"Face processing MEEG dataset with HED annotation\",\n  \"license\": \"CC0\",\n  \"dataset_type\": \"raw\",\n  \"authors\": {\n    \"Daniel G. Wakeman\": {},\n    \"Richard N Henson\": {},\n    \"Dung Truong (curation)\": {},\n    \"Kay Robbins (curation)\": {},\n    \"Scott Makeig (curation)\": {},\n    \"Arno Delorme (curation)\": {}\n  },\n  \"related_identifiers\": [\n    {\n      \"identifier\": \"10.18112/openneuro.ds003645.v2.0.2\",\n      \"identifier_type\": \"DOI\",\n      \"relation_type\": \"IsDerivedFrom\"\n    },\n    {\n      \"identifier\": \"10.1038/sdata.2015.1\",\n      \"identifier_type\": \"DOI\",\n      \"relation_type\": \"References\"\n    },\n    {\n      \"identifier\": \"10.1007/s12021-021-09537-4. Online: https://link.springer.com/article/10.1007/s12021-021-09537-4.\",\n      \"identifier_type\": \"DOI\",\n      \"relation_type\": \"References\"\n    },\n    {\n      \"identifier\": \"https://github.com/nemarDatasets/on003645\",\n      \"identifier_type\": \"URL\",\n      \"relation_type\": \"IsDescribedBy\"\n    },\n    {\n      \"identifier\": \"https://nemar.org/dataexplorer/detail?dataset_id=on003645\",\n      \"identifier_type\": \"URL\",\n      \"relation_type\": \"IsDescribedBy\"\n    },\n    {\n      \"identifier\": \"10.1007/s12021-021-09537-4\",\n      \"identifier_type\": \"DOI\",\n      \"relation_type\": \"IsDescribedBy\"\n    }\n  ],\n  \"funding_references\": [\n    {\n      \"funder_name\": \"UK Medical Research Council\",\n      \"award_number\": \"MC_A060_5PR10\"\n    },\n    {\n      \"funder_name\": \"Elekta Ltd.\"\n    },\n    {\n      \"funder_name\": \"Army Research Laboratory\",\n      \"award_number\": \"W911NF-10-2-0022\"\n    },\n    {\n      \"funder_name\": \"NIH\",\n      \"award_number\": \"R01 EB023297-03\"\n    },\n    {\n      \"funder_name\": \"NIH\",\n      \"award_number\": \"R01 NS047293-l4\"\n    },\n    {\n      \"funder_name\": \"NIH\",\n      \"award_number\": \"R24 MH120037-01\"\n    }\n  ],\n  \"modalities\": [\n    \"anat\",\n    \"beh\",\n    \"eeg\",\n    \"meg\"\n  ],\n  \"resource_type_specific\": \"Structural MRI Dataset\",\n  \"sizes\": [\n    \"58.5 GB (823 files)\"\n  ],\n  \"formats\": [\n    \".bmp\",\n    \".fdt\",\n    \".fif\",\n    \".gitattributes\",\n    \".gz\",\n    \".json\",\n    \".m\",\n    \".md\",\n    \".pos\",\n    \".set\",\n    \".tsv\",\n    \".yml\"\n  ],\n  \"description\": \"This dataset comprises simultaneous MEG and EEG recordings from 18 participants performing a face processing task with perceptual symmetry judgments on famous, unfamiliar, and scrambled faces across two sessions. The data includes structural MRI scans to support source localization and has been annotated with Hierarchical Event Descriptors (HED v8.0.0) to enhance machine-readability and FAIR compliance. The dataset represents a curated and repackaged version of the original multi-modal ds000117 study, with preprocessed EEG data converted to EEGLAB format.\",\n  \"methods_description\": \"Eighteen participants completed two recording sessions three months apart. During each session, participants performed a perceptual task responding to photographs of famous, unfamiliar, and scrambled faces by pressing keyboard keys to indicate subjective spatial symmetry judgments. MEG and EEG data were simultaneously recorded during one session; structural MRI scans were acquired during a separate session. Each face was presented twice, with presentation timing varied (immediate or delayed by 5-15 presentations). EEG preprocessing included channel extraction from MEG/EEG data, fiducial addition, channel renaming, event extraction and correction (34 ms latency shift), removal of spurious event types, and HED annotation.\",\n  \"keywords\": [\n    {\n      \"term\": \"EEG\"\n    },\n    {\n      \"term\": \"MEG\"\n    },\n    {\n      \"term\": \"face processing\"\n    },\n    {\n      \"term\": \"famous faces\"\n    },\n    {\n      \"term\": \"unfamiliar faces\"\n    },\n    {\n      \"term\": \"scrambled faces\"\n    },\n    {\n      \"term\": \"symmetry judgment\"\n    },\n    {\n      \"term\": \"Event-Related Potentials, P300\",\n      \"subject_scheme\": \"MeSH\",\n      \"scheme_uri\": \"https://id.nlm.nih.gov/mesh/\",\n      \"value_uri\": \"http://id.nlm.nih.gov/mesh/D018913\"\n    },\n    {\n      \"term\": \"Hierarchical Event Descriptors\"\n    },\n    {\n      \"term\": \"source localization\"\n    },\n    {\n      \"term\": \"multimodal neuroimaging\"\n    },\n    {\n      \"term\": \"structural MRI\"\n    },\n    {\n      \"term\": \"simultaneous recording\"\n    },\n    {\n      \"term\": \"EEGLAB format\"\n    }\n  ],\n  \"source_hash\": \"248c8f4de38a740d2c51311ac7b2a8b3cac14b96e4adeb49adbbca89b54f5577\"\n}","last_activity_at":"2026-09-15 15:51:02","source":"openneuro","source_id":"ds003645","subject_count":18,"modalities":"anat,beh,eeg,meg","age_min":23,"age_max":37,"file_size":114157580432,"total_files":1145,"tasks":"FacePerception,FaceRecognition,noise","metadata_columns_error":null,"staleness_warn_stage":null,"staleness_admin_notified_at":null,"authors":"Daniel G. 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