{"dataset":{"id":"54501","dataset_id":"on003766","name":"A resource for assessing dynamic binary choices in the adult brain using EEG and mouse-tracking","description":"This dataset combines high-density electroencephalography (128-channel HD-EEG) and mouse-tracking to examine dynamic decision-making processes in the human brain. Collected from 31 adults (ages 18-33), it includes resting-state and task-related EEG data acquired during food preference choices and semantic judgment tasks. The resource provides both raw and preprocessed EEG data with synchronized behavioral measures, enabling investigation of neural correlates underlying binary choice decisions.","owner_user_id":15,"status":"active","github_repo":"nemarDatasets/on003766","concept_doi":"10.82901/nemar.on003766","latest_version_doi":"10.82901/nemar.on003766.v1.0.0","created_at":"2026-06-22 15:01:24","updated_at":"2026-07-22 02:19:49","zenodo_concept_id":null,"is_sandbox":0,"visibility":"public","ezid_status":"public","enrichment_json":"{\n  \"version\": \"2.0\",\n  \"pipeline_stage\": \"enriched\",\n  \"title\": \"A resource for assessing dynamic binary choices in the adult brain using EEG and mouse-tracking\",\n  \"description\": \"This dataset combines high-density electroencephalography (128-channel HD-EEG) and mouse-tracking to examine dynamic decision-making processes in the human brain. Collected from 31 adults (ages 18-33), it includes resting-state and task-related EEG data acquired during food preference choices and semantic judgment tasks. The resource provides both raw and preprocessed EEG data with synchronized behavioral measures, enabling investigation of neural correlates underlying binary choice decisions.\",\n  \"methods_description\": \"EEG data were acquired using a 128-channel cap based on the standard 10/20 system with an Electrical Geodesics Inc (EGI) system at a sampling rate of 1000 Hz, with the E129 (Cz) electrode as reference and electrode impedances maintained below 50 kΩ. Participants performed food preference choice and semantic judgment tasks while mouse-tracking was recorded concurrently. Preprocessing was performed using EEGLAB.\",\n  \"license\": \"CC0\",\n  \"dataset_type\": \"raw\",\n  \"authors\": {\n    \"Kun Chen\": {\n      \"orcid\": \"0000-0002-9138-3195\"\n    },\n    \"Ruien Wang\": {},\n    \"Jiamin Huang\": {},\n    \"Fei Gao\": {},\n    \"Zhen Yuan\": {},\n    \"Yanyan Qi\": {\n      \"orcid\": \"0000-0001-9954-6588\"\n    },\n    \"Haiyan Wu\": {}\n  },\n  \"keywords\": [\n    {\n      \"term\": \"Electroencephalography\",\n      \"subject_scheme\": \"MeSH\",\n      \"value_uri\": \"http://id.nlm.nih.gov/mesh/D004569\"\n    },\n    {\n      \"term\": \"Decision Making\",\n      \"subject_scheme\": \"MeSH\",\n      \"value_uri\": \"http://id.nlm.nih.gov/mesh/D003657\"\n    },\n    {\n      \"term\": \"Mouse-tracking\"\n    },\n    {\n      \"term\": \"Resting state\"\n    },\n    {\n      \"term\": \"Food preferences\"\n    },\n    {\n      \"term\": \"Semantic judgment\"\n    },\n    {\n      \"term\": \"Binary choice\"\n    }\n  ],\n  \"related_identifiers\": [\n    {\n      \"identifier\": \"10.18112/openneuro.ds003766.v2.0.3\",\n      \"identifier_type\": \"DOI\",\n      \"relation_type\": \"IsDerivedFrom\"\n    },\n    {\n      \"identifier\": \"10.1038/s41597-022-01538-5\",\n      \"identifier_type\": \"DOI\",\n      \"relation_type\": \"References\"\n    },\n    {\n      \"identifier\": \"https://github.com/nemarDatasets/on003766\",\n      \"identifier_type\": \"URL\",\n      \"relation_type\": \"IsDescribedBy\"\n    },\n    {\n      \"identifier\": \"https://nemar.org/dataset/on003766\",\n      \"identifier_type\": \"URL\",\n      \"relation_type\": \"IsDescribedBy\"\n    }\n  ],\n  \"resource_type_general\": \"Dataset\",\n  \"resource_type_specific\": \"Neuroimaging Dataset\",\n  \"modalities\": [\n    \"beh\",\n    \"eeg\"\n  ],\n  \"sizes\": [\n    \"163.8 GB (1898 files)\"\n  ],\n  \"formats\": [\n    \".bin\",\n    \".csv\",\n    \".fdt\",\n    \".gitattributes\",\n    \".jpg\",\n    \".json\",\n    \".lch\",\n    \".log\",\n    \".md\",\n    \".mff/Contents/PkgInfo\",\n    \".plist\",\n    \".png\",\n    \".psydat\",\n    \".psyexp\",\n    \".py\",\n    \".rtf\",\n    \".set\",\n    \".tsv\",\n    \".txt\",\n    \".xml\",\n    \".yml\"\n  ],\n  \"funding_references\": [\n    {\n      \"funder_name\": \"National Natural Science Foundation of China\",\n      \"award_number\": \"81871504\"\n    },\n    {\n      \"funder_name\": \"National Natural Science Foundation of China\",\n      \"award_number\": \"82071935\"\n    }\n  ],\n  \"source_hash\": \"395cf12c385729d2e4db4907849b211eb7f93e5a7d1769a03d3e440fcd0bf17b\"\n}","last_activity_at":"2026-06-22 15:01:24","source":"openneuro","source_id":"ds003766","subject_count":31,"modalities":"beh,eeg","age_min":18,"age_max":33,"file_size":164033760771,"total_files":4561,"tasks":"foodchoice,foodend,foodhealthy,foodtaste,imagechoice,resting,wordchoice","metadata_columns_error":null,"staleness_warn_stage":null,"staleness_admin_notified_at":null,"authors":"Kun Chen, Ruien Wang, Jiamin Huang, Fei Gao, Zhen Yuan, Yanyan Qi, Haiyan Wu","license":"CC0","readme":"[![DOI](https://img.shields.io/badge/DOI-10.82901%2Fnemar.on003766-blue)](https://doi.org/10.82901/nemar.on003766)\n\n# A resource for assessing dynamic binary choices in the adult brain using EEG and mouse-tracking\n\n## Description\n\nThis dataset was collected in 2020, which combines high-density Electroencephalography (HD-EEG, 128 channels) and mouse-tracking intended as a resource for examining the dynamic decision process of semantics and preference choices in the human brain. The dataset includes high-density resting-state and task-related (food preference choices and semantic judgments) EEG acquired from 31 individuals (ages: 18-33).\n\n## EEG acquisition\n\nThe EEG data were acquired using a 128-channel cap based on the standard 10/20 System with Electrical Geodesics Inc (EGI, Eugene, Oregon) system. During recording, sampling rate was 1000Hz, and the E129 (Cz) electrode was used as reference. Electrode impedances were kept below 50kohm for each electrode during the experiment.\n\n## Main files\n\n**`sub-*`**: EEG (`.set`) and behavior data with BIDS format.\n\n**`sourcedata/rawdata`**: Raw `.mff` EGI data and behavior data with subject information desensitization.\n\n**`sourcedata/psychopy`**: Stimuli and PsychoPy scripts for presentation.\n\n**`derivatives/eeglab-preproc`**: Preprocessed continuous EEG data with EEGLAB (Easy to set different epoch time windows for further analysis).\n\n## Others\n\nPlease refer to the [corresponding paper](https://doi.org/10.1038/s41597-022-01538-5) and [GitHub code](https://github.com/andlab-um/MT-EEG-dataset) to get more details.\n\n## References\n\nChen, K., Wang, R., Huang, J., Gao, F., Yuan, Z., Qi, Y., & Wu, H. (2022). A resource for assessing dynamic binary choices in the adult brain using EEG and mouse-tracking. Scientific Data, 9(1), 416. https://doi.org/10.1038/s41597-022-01538-5\n\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\n\nPernet, C. R., Appelhoff, S., Gorgolewski, K. J., Flandin, G., Phillips, C., Delorme, A., Oostenveld, R. (2019). EEG-BIDS, an extension to the brain imaging data structure for electroencephalography. Scientific Data, 6, 103. https://doi.org/10.1038/s41597-019-0104-8\n","bids_version":"1.6.0","sessions_count":null,"publish_date":null,"embedding_dirty":0,"license_tier":"public","zarr_status":"ready","zarr_converted_at":"2026-08-04 17:55:04","zarr_store_count":217,"zarr_index_etag":"7ccc44e3d3095280ed4a8c3ec92f152d","zarr_source_commit":"5e3240af35041254ac1f6356938ebab7eb1b36e2","archive_status":null,"archive_size":null,"archive_retry_count":0,"records_status":"ready","archive_skip_reason":"dataset 152.8 GB exceeds 100.0 GB archive limit; use direct download","zarr_errors":31,"zarr_failure_count":31,"zarr_deterministic":1,"zarr_failed_at":"2026-08-04 17:55:04","num_dataset_citations":1,"num_datapaper_citations":4,"n_channels":129,"electrode_system":"egi-geodesic","has_hed":0,"hed_version":null,"is_exemplar":0,"bytes_present":163819089701,"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":252459.05600000013,"recording_duration_min":773.36,"recording_duration_max":1955.048,"recording_count":248,"recordings_unavailable":31,"recordings_measured":217,"channel_count_min":101,"channel_count_max":129,"sampling_frequency":1000,"power_line_frequency":50,"eeg_reference":"E129 (Cz)","placement_scheme":null,"sweep_stamps":"{\"enrichment_updated_at\":\"2026-07-22 02:19:38\",\"metadata_updated_at\":\"2026-07-22 02:19:48\",\"archive_checked_at\":\"2026-06-22 15:20:49\",\"zarr_checked_at\":null,\"records_checked_at\":\"2026-06-22 15:21:25\",\"citations_updated_at\":\"2026-09-08 03:00:50\",\"channel_montage_checked_at\":\"2026-06-28 23:15:18\",\"hed_checked_at\":\"2026-06-30 04:47:22\",\"data_checked_at\":null,\"availability_report_at\":\"2026-07-23 01:15:09\",\"recording_stats_at\":\"2026-09-02 11:32:33\",\"signal_defaults_at\":\"2026-09-02 12:03:11\"}","participants":31,"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":"153 GB","zarr_data_failures":{"count":31,"detail_ref":"zarr/index.json","compacted_by":"migration_0074"},"zarr_index_url":"https://zarr.nemar.org/on003766/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}}