{"dataset":{"id":"51215","dataset_id":"on002722","name":"A dataset recorded during development of an affective brain-computer music interface: calibration session","description":"This dataset contains electroencephalogram (EEG), galvanic skin response (GSR), and electrocardiogram (ECG) recordings from 19 healthy adult participants during a calibration session of an affective brain-computer music interface system. Participants listened to 40-second synthetic music clips designed to induce specific affective states defined by valence and arousal dimensions across 5 runs of 18 trials each. The synthetic music was generated in real-time based on target emotional states and could be modified online to induce target emotional states. The dataset includes self-reported affective state ratings and auxiliary variables, serving as calibration data for the brain-computer music interface system. This dataset is part of a three-dataset collection that includes offline training and online real-time control sessions.","owner_user_id":15,"status":"active","github_repo":"nemarDatasets/on002722","concept_doi":"10.82901/nemar.on002722","latest_version_doi":"10.82901/nemar.on002722.v1.0.0","created_at":"2026-06-21 08:01:05","updated_at":"2026-07-10 22:11:18","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 dataset recorded during development of an affective brain-computer music interface: calibration session\",\n  \"description\": \"This dataset contains electroencephalogram (EEG), galvanic skin response (GSR), and electrocardiogram (ECG) recordings from 19 healthy adult participants during a calibration session of an affective brain-computer music interface system. Participants listened to 40-second synthetic music clips designed to induce specific affective states defined by valence and arousal dimensions across 5 runs of 18 trials each. The synthetic music was generated in real-time based on target emotional states and could be modified online to induce target emotional states. The dataset includes self-reported affective state ratings and auxiliary variables, serving as calibration data for the brain-computer music interface system. This dataset is part of a three-dataset collection that includes offline training and online real-time control sessions.\",\n  \"methods_description\": \"EEG, GSR, and ECG data were recorded at 1 kHz sampling rate from 19 healthy adult participants during listening to synthetic music clips (40 s duration). Each clip targeted two affective states sequentially (first 20 s for state 1, remaining 20 s for state 2), with 5 runs of 18 music trials per session. The synthetic music clips were generated in real-time by a music generator based on target emotional states defined by valence (LOW/NEUTRAL/HIGH) and arousal (LOW/NEUTRAL/HIGH) dimensions. Participants provided self-reported valence and arousal ratings for each trial.\",\n  \"license\": \"CC0\",\n  \"dataset_type\": \"raw\",\n  \"authors\": {\n    \"Ian Daly\": {},\n    \"Nicoletta Nicolaou\": {},\n    \"Duncan Williams\": {\n      \"orcid\": \"0000-0003-4793-8330\",\n      \"affiliations\": [\n        {\n          \"name\": \"Plymouth University, Plymouth, Devon, UK\"\n        }\n      ]\n    },\n    \"Faustina Hwang\": {},\n    \"Alexis Kirke\": {},\n    \"Eduardo Miranda\": {},\n    \"Slawomir J. Nasuto\": {}\n  },\n  \"keywords\": [\n    {\n      \"term\": \"EEG\",\n      \"subject_scheme\": \"MeSH\",\n      \"value_uri\": \"http://id.nlm.nih.gov/mesh/D004569\"\n    },\n    {\n      \"term\": \"Brain-Computer Interfaces\",\n      \"subject_scheme\": \"MeSH\",\n      \"value_uri\": \"http://id.nlm.nih.gov/mesh/D062207\"\n    },\n    {\n      \"term\": \"emotion recognition\"\n    },\n    {\n      \"term\": \"music perception\"\n    },\n    {\n      \"term\": \"affective computing\"\n    },\n    {\n      \"term\": \"galvanic skin response\"\n    },\n    {\n      \"term\": \"Galvanic Skin Response\",\n      \"subject_scheme\": \"MeSH\",\n      \"value_uri\": \"http://id.nlm.nih.gov/mesh/D005712\"\n    },\n    {\n      \"term\": \"Electrocardiography\",\n      \"subject_scheme\": \"MeSH\",\n      \"value_uri\": \"http://id.nlm.nih.gov/mesh/D004562\"\n    },\n    {\n      \"term\": \"Emotions\",\n      \"subject_scheme\": \"MeSH\",\n      \"value_uri\": \"http://id.nlm.nih.gov/mesh/D004644\"\n    },\n    {\n      \"term\": \"calibration\"\n    },\n    {\n      \"term\": \"valence\"\n    },\n    {\n      \"term\": \"arousal\"\n    },\n    {\n      \"term\": \"synthetic music\"\n    }\n  ],\n  \"related_identifiers\": [\n    {\n      \"identifier\": \"https://github.com/nemarDatasets/on002722\",\n      \"identifier_type\": \"URL\",\n      \"relation_type\": \"IsDescribedBy\"\n    },\n    {\n      \"identifier\": \"https://nemar.org/dataexplorer/detail?dataset_id=on002722\",\n      \"identifier_type\": \"URL\",\n      \"relation_type\": \"IsDescribedBy\"\n    },\n    {\n      \"identifier\": \"10.1145/3059005\",\n      \"identifier_type\": \"DOI\",\n      \"relation_type\": \"References\"\n    },\n    {\n      \"identifier\": \"10.1038/sdata.2018.203\",\n      \"identifier_type\": \"DOI\",\n      \"relation_type\": \"IsDescribedBy\"\n    },\n    {\n      \"identifier\": \"10.18112/openneuro.ds002722.v1.0.1\",\n      \"identifier_type\": \"DOI\",\n      \"relation_type\": \"IsDerivedFrom\"\n    }\n  ],\n  \"funding_references\": [\n    {\n      \"funder_name\": \"Engineering and Physical Sciences Research Council\",\n      \"award_number\": \"EP/J003077/1\"\n    },\n    {\n      \"funder_name\": \"Engineering and Physical Sciences Research Council\",\n      \"award_number\": \"EP/J002135/1\"\n    }\n  ],\n  \"resource_type_specific\": \"EEG Dataset\",\n  \"modalities\": [\n    \"eeg\"\n  ],\n  \"sizes\": [\n    \"6.5 GB (312 files)\"\n  ],\n  \"formats\": [\n    \".edf\",\n    \".gitattributes\",\n    \".json\",\n    \".m\",\n    \".md\",\n    \".tsv\",\n    \".wav\",\n    \".yml\"\n  ],\n  \"source_hash\": \"2f3839ab548fda661d2a018dfcf4c862812fb1ef5be621221e92daa8f45fa755\"\n}","last_activity_at":"2026-06-21 08:01:05","source":"openneuro","source_id":"ds002722","subject_count":19,"modalities":"eeg","age_min":19,"age_max":30,"file_size":6545824279,"total_files":696,"tasks":"run1,run2,run3,run4,run5","metadata_columns_error":null,"staleness_warn_stage":null,"staleness_admin_notified_at":null,"authors":"Ian Daly, Nicoletta Nicolaou, Duncan Williams, Faustina Hwang, Alexis Kirke, Eduardo Miranda, Slawomir J. Nasuto","license":"CC0","readme":"[![DOI](https://img.shields.io/badge/DOI-10.82901%2Fnemar.on002722-blue)](https://doi.org/10.82901/nemar.on002722)\n\n0. Sections\r\n------------\r\n\r\n1. Project\r\n2. Dataset\r\n3. Terms of Use\r\n4. Contents\r\n5. Method and Processing\r\n\r\n1. PROJECT\r\n------------\r\n\r\nTitle: Brain-Computer Music Interface for Monitoring and Inducing Affective States (BCMI-MIdAS)\r\nDates: 2012-2017\r\nFunding organisation: Engineering and Physical Sciences Research Council (EPSRC)\r\nGrant no.: EP/J003077/1 and EP/J002135/1.\r\n\r\n2. DATASET\r\n------------\r\n\r\nEEG data from an affective Music Brain-Computer Interface: system calibration.\r\n\r\nDescription: This dataset accompanies the publication by Daly et al. (2018) and has been analysed in Daly et al. (2015) (please see Section 5 for full references). The purpose of the research activity in which the data were collected was to calibrate an affective brain-computer interface system to induce specific affective states by real-time online modification of synthetic music. \r\n\r\nFor this purpose, 20 healthy adult volunteers listened to music clips (40 s) targeting two affective states, as defined by valence and arousal (the first 20-s targeted state 1, while the remaining 20-s targeted state 2). Data were recorded over 1 session with 5 runs of 18 music trials each. The music clips were generated using a synthetic music generator.\r\n\r\nThe dataset contains the electroencephalogram (EEG), galvanic skin response (GSR) and electrocardiogram (ECG) data from 19 healthy adult participants while listening to the music clips, together with the reported affective state (valence and arousal values) and auxiliary variables. \r\n\r\nThis dataset is connected to 2 additional datasets: \r\n\r\n1.\tEEG data from an affective Music Brain-Computer Interface: offline training to induce target emotional states. doi:\r\n2.\tEEG data from an affective Music Brain-Computer Interface: online real-time control. doi:\r\nPlease note that the number of participants varies between datasets; however, participant codes are the same across all three datasets.\r\n\r\nPublication Year: 2018\r\n\r\nCreators: Nicoletta Nicolaou, Ian Daly\r\n\r\nContributors: Isil Poyraz Bilgin, James Weaver, Asad Malik, Alexis Kirke, Duncan Williams.\r\n\r\nPrincipal Investigator: Slawomir Nasuto(EP/J003077/1).\r\n\r\nCo-Investigator: Eduardo Miranda (EP/J002135/1).\r\n\r\nOrganisation: University of Reading\r\n\r\nRights-holders: University of Reading\r\n\r\nSource: The synthetic generator used to generate the music clips was presented in Williams et al., “Affective Calibration of Musical Feature Sets in an Emotionally Intelligent Music Composition System”, ACM Trans. Appl. Percept. 14, 3, Article 17 (May 2017), 13 pages. DOI: https://doi.org/10.1145/3059005\r\n\r\n3. TERMS OF USE\r\n-----------------\r\n\r\nCopyright University of Reading, 2018. This dataset is licensed by the rights-holder(s) under a Creative Commons Attribution 4.0 International Licence: https://creativecommons.org/licenses/by/4.0/.\r\n\r\n4. CONTENTS\r\n------------\r\n\r\nThe dataset comprises of data from 19 subjects. The sampling rate is 1 kHz and the music listening task corresponding to a music clip is 40 s long (clip duration). The 40-s music clip is generated in real-time by the music generator, based on the target emotional state (defined by LOW/NEUTRAL/HIGH valence and LOW/NEUTRAL/HIGH arousal).\r\n\r\n5. METHOD and PROCESSING\r\n--------------------------\r\n\r\nThis information is available in the following publications:\r\n\r\n[1] Daly, I., Nicolaou, N., Williams, D., Hwang, F., Kirke, A., Miranda, E., Nasuto, S.J., �Neural and physiological data from participants listening to affective music�, Scientific Data, 2018.\r\n[2] Daly, I., Williams, D., Hwang, F., Kirke, A., Malik, A., Roesch, E., Weaver, J., Miranda, E. R., Nasuto, S. J., �Identifying music-induced emotions from EEG for use in brain-computer music interfacing�, in Proc. 4th Workshop on Affective Brain-Computer Interfaces at the 6th International Conference on Affective Computing and Intelligent Interaction (ACII2015). Xi�an, China, 21-25 September 2015.\r\nIf you use this dataset in your study please cite these references, as well as the following reference:\r\n[3] Williams, D., Kirke, A., Miranda, E.R., Daly, I., Hwang, F., Weaver, J., Nasuto, S.J., �Affective Calibration of Musical Feature Sets in an Emotionally Intelligent Music Composition System�, ACM Trans. Appl. Percept. 14, 3, Article 17 (May 2017), 13 pages. DOI: https://doi.org/10.1145/3059005\r\n\r\nThank you for your interest in our work.","bids_version":"1.0.2","sessions_count":null,"publish_date":null,"embedding_dirty":0,"license_tier":"public","zarr_status":"failed","zarr_converted_at":null,"zarr_store_count":null,"zarr_index_etag":null,"zarr_source_commit":null,"archive_status":"ready","archive_size":5662728829,"archive_retry_count":0,"records_status":"ready","archive_skip_reason":null,"zarr_errors":94,"zarr_failure_count":94,"zarr_deterministic":1,"zarr_failed_at":"2026-09-06 13:30:04","num_dataset_citations":0,"num_datapaper_citations":0,"n_channels":32,"electrode_system":"10-10","has_hed":0,"hed_version":null,"is_exemplar":0,"bytes_present":6545289110,"data_complete":1,"withdrawn_at":null,"withdrawn_reason":null,"archive_complete":null,"archive_absent_files":null,"archive_declared_files":null,"zarr_pool_breaks":0,"total_recording_duration":null,"recording_duration_min":null,"recording_duration_max":null,"recording_count":null,"recordings_unavailable":null,"recordings_measured":null,"channel_count_min":null,"channel_count_max":null,"sampling_frequency":1000,"power_line_frequency":50,"eeg_reference":"placed on FCz","placement_scheme":null,"sweep_stamps":"{\"enrichment_updated_at\":\"2026-06-21 08:09:45\",\"metadata_updated_at\":\"2026-06-21 08:09:55\",\"archive_checked_at\":\"2026-06-21 08:14:59\",\"zarr_checked_at\":null,\"records_checked_at\":\"2026-06-21 08:12:38\",\"citations_updated_at\":\"2026-09-08 03:00:53\",\"channel_montage_checked_at\":\"2026-06-28 23:08:02\",\"hed_checked_at\":\"2026-06-30 04:39:02\",\"data_checked_at\":\"2026-07-29 03:00:15\",\"availability_report_at\":\"2026-07-23 01:12:22\",\"recording_stats_at\":null,\"signal_defaults_at\":\"2026-09-02 11:56:20\"}","participants":19,"num_citations":0,"latest_version":"v1.0.0","zarr_verify_status":null,"zarr_verified_at":null,"owner_username":"nemarAdmin","owner_github":"nemarAdmin","file_size_formatted":"6.10 GB","zarr_data_failures":{"count":94,"detail_ref":"zarr/index.json","pending":0,"discovered":94},"zarr_index_url":null,"attestation_deposit_type":null,"attestation_key_status":null,"attestation_deidentified":null,"attestation_no_duplicate":null,"attestation_upstream_source":null,"attestation_accepted_at":null}}