{"dataset":{"id":"61229","dataset_id":"on007353","name":"HAD-MEEG","description":"HAD-MEEG is a magnetoencephalography (MEG) and electroencephalography (EEG) dataset recorded from 30 participants viewing 21,600 video clips spanning 180 categories of human action, extending the previously released Human Action Dataset (HAD) fMRI resource. It was collected in the same participants and with the same stimuli as HAD-fMRI to enable combined spatiotemporal investigation of neural mechanisms underlying human action recognition. The dataset leverages the millisecond-level temporal resolution of M/EEG to complement the spatial precision of fMRI.","owner_user_id":15,"status":"active","github_repo":"nemarDatasets/on007353","concept_doi":"10.82901/nemar.on007353","latest_version_doi":"10.82901/nemar.on007353.v1.0.0","created_at":"2026-06-30 06:31:41","updated_at":"2026-08-18 23:54:31","zenodo_concept_id":null,"is_sandbox":0,"visibility":"public","ezid_status":"public","enrichment_json":"{\n  \"version\": \"2.0\",\n  \"pipeline_stage\": \"validated\",\n  \"title\": \"HAD-MEEG\",\n  \"description\": \"HAD-MEEG is a magnetoencephalography (MEG) and electroencephalography (EEG) dataset recorded from 30 participants viewing 21,600 video clips spanning 180 categories of human action, extending the previously released Human Action Dataset (HAD) fMRI resource. It was collected in the same participants and with the same stimuli as HAD-fMRI to enable combined spatiotemporal investigation of neural mechanisms underlying human action recognition. The dataset leverages the millisecond-level temporal resolution of M/EEG to complement the spatial precision of fMRI.\",\n  \"methods_description\": \"MEG and EEG data were recorded from 30 participants as they viewed 21,600 video clips spanning 180 categories of human action, using the same participants and stimuli as the previously released HAD-fMRI dataset.\",\n  \"license\": \"CC0\",\n  \"dataset_type\": \"raw\",\n  \"authors\": {\n    \"Guohao Zhang\": {},\n    \"Sai Ma\": {},\n    \"Ming Zhou\": {},\n    \"Shaohua Tang\": {},\n    \"Shuyi Zhen\": {},\n    \"Zheng Li\": {},\n    \"Zonglei Zhen\": {}\n  },\n  \"keywords\": [\n    {\n      \"term\": \"EEG\"\n    },\n    {\n      \"term\": \"MEG\"\n    },\n    {\n      \"term\": \"human action recognition\"\n    },\n    {\n      \"term\": \"Social Cognition\",\n      \"subject_scheme\": \"MeSH\",\n      \"value_uri\": \"http://id.nlm.nih.gov/mesh/D000083282\"\n    },\n    {\n      \"term\": \"Visual Perception\",\n      \"subject_scheme\": \"MeSH\",\n      \"value_uri\": \"http://id.nlm.nih.gov/mesh/D014796\"\n    },\n    {\n      \"term\": \"neuroimaging\"\n    }\n  ],\n  \"related_identifiers\": [\n    {\n      \"identifier\": \"10.18112/openneuro.ds004488.v2.0.0\",\n      \"identifier_type\": \"DOI\",\n      \"relation_type\": \"IsDerivedFrom\"\n    },\n    {\n      \"identifier\": \"https://github.com/nemarDatasets/on007353\",\n      \"identifier_type\": \"URL\",\n      \"relation_type\": \"IsDescribedBy\"\n    },\n    {\n      \"identifier\": \"https://nemar.org/dataset/on007353\",\n      \"identifier_type\": \"URL\",\n      \"relation_type\": \"IsDescribedBy\"\n    },\n    {\n      \"identifier\": \"10.18112/openneuro.ds007353.v1.0.0\",\n      \"identifier_type\": \"DOI\",\n      \"relation_type\": \"IsDerivedFrom\"\n    }\n  ],\n  \"funding_references\": [\n    {\n      \"funder_name\": \"Beijing Natural Science Foundation\",\n      \"award_number\": \"L247010\"\n    },\n    {\n      \"funder_name\": \"National Natural Science Foundation of China\",\n      \"award_number\": \"62433015\"\n    },\n    {\n      \"funder_name\": \"National Natural Science Foundation of China\",\n      \"award_number\": \"31771251\"\n    },\n    {\n      \"funder_name\": \"STI 2030-Major Projects of the Ministry of Science and Technology of China\",\n      \"award_number\": \"2021ZD0200407\"\n    }\n  ],\n  \"resource_type_general\": \"Dataset\",\n  \"resource_type_specific\": \"EEG Dataset\",\n  \"modalities\": [\n    \"eeg\",\n    \"meg\",\n    \"anat\"\n  ],\n  \"sizes\": [\n    \"235.1 GB (23087 files)\"\n  ],\n  \"formats\": [\n    \".csv\",\n    \".fif\",\n    \".gz\",\n    \".json\",\n    \".md\",\n    \".mp4\",\n    \".set\",\n    \".tsv\",\n    \".yml\"\n  ],\n  \"source_hash\": \"3c810982b7c56c5565d58669b816e0e3c956839b9a9eea983ad9b602b11fefe9\"\n}","last_activity_at":"2026-06-30 06:31:41","source":"openneuro","source_id":"ds007353","subject_count":32,"modalities":"anat,eeg,meg","age_min":18,"age_max":31,"file_size":237300545996,"total_files":48644,"tasks":"action,noise","metadata_columns_error":null,"staleness_warn_stage":null,"staleness_admin_notified_at":null,"authors":"Guohao Zhang, Sai Ma, Ming Zhou, Shaohua Tang, Shuyi Zhen, Zheng Li, Zonglei Zhen","license":"CC0","readme":"[![DOI](https://img.shields.io/badge/DOI-10.82901%2Fnemar.on007353-blue)](https://doi.org/10.82901/nemar.on007353)\n\nHuman action recognition is a core component of social cognition, engaging spatially distributed and temporally evolving neural responses that encode visual information and infer intention. To map the brain’s spatial organization supporting this process, we previously released the Human Action Dataset (HAD), a functional magnetic resonance imaging (fMRI) resource. However, fMRI’s limited temporal resolution constrains its ability to capture rapid neural dynamics. Here, we present the HAD-MEEG dataset, which extends HAD-fMRI, leveraging the millisecond-level temporal resolution of magnetoencephalography (MEG) and electroencephalography (EEG). HAD-MEEG were recorded in the same participants and with the same stimuli as HAD-fMRI, in which 30 participants viewed 21,600 video clips spanning 180 categories of human action. By integrating the temporal precision of M/EEG with the spatial precision of fMRI, HAD enables comprehensive spatiotemporal investigation of the neural mechanisms underlying human action recognition.","bids_version":"1.10.1","sessions_count":12,"publish_date":null,"embedding_dirty":0,"license_tier":"public","zarr_status":"ready","zarr_converted_at":"2026-08-23 05:16:30","zarr_store_count":473,"zarr_index_etag":"df8349952d7acf5b6358a4875ac9cd85","zarr_source_commit":"3b4bcdbd5f4addfd90a01b7a1d3b47dabb645cc8","archive_status":null,"archive_size":null,"archive_retry_count":0,"records_status":"ready","archive_skip_reason":null,"zarr_errors":0,"zarr_failure_count":0,"zarr_deterministic":0,"zarr_failed_at":null,"num_dataset_citations":5,"num_datapaper_citations":0,"n_channels":62,"electrode_system":"10-10","has_hed":0,"hed_version":null,"is_exemplar":0,"bytes_present":235108553819,"data_complete":1,"withdrawn_at":null,"withdrawn_reason":null,"archive_complete":null,"archive_absent_files":null,"archive_declared_files":null,"zarr_pool_breaks":null,"total_recording_duration":161376.08800000013,"recording_duration_min":34.132,"recording_duration_max":415.896,"recording_count":473,"recordings_unavailable":0,"recordings_measured":473,"channel_count_min":64,"channel_count_max":409,"sampling_frequency":1000,"power_line_frequency":50,"eeg_reference":null,"placement_scheme":"based on the extended 10/20 system","sweep_stamps":"{\"enrichment_updated_at\":\"2026-08-18 23:53:59\",\"metadata_updated_at\":\"2026-08-18 23:54:29\",\"archive_checked_at\":null,\"zarr_checked_at\":null,\"records_checked_at\":\"2026-06-30 07:21:25\",\"citations_updated_at\":\"2026-09-08 03:00:50\",\"channel_montage_checked_at\":null,\"hed_checked_at\":\"2026-06-30 07:33:59\",\"data_checked_at\":null,\"availability_report_at\":\"2026-07-23 01:32:19\",\"recording_stats_at\":\"2026-09-02 11:34:00\",\"signal_defaults_at\":\"2026-09-02 12:54:12\"}","participants":32,"num_citations":5,"latest_version":"v1.0.0","zarr_verify_status":null,"zarr_verified_at":null,"owner_username":"nemarAdmin","owner_github":"nemarAdmin","file_size_formatted":"221 GB","zarr_data_failures":null,"zarr_index_url":"https://zarr.nemar.org/on007353/zarr/index.json","attestation_deposit_type":null,"attestation_key_status":null,"attestation_deidentified":null,"attestation_no_duplicate":null,"attestation_upstream_source":null,"attestation_accepted_at":null}}