{"dataset":{"id":"51217","dataset_id":"on002724","name":"A dataset recorded during development of an affective brain-computer music interface: training sessions","description":"This dataset contains electroencephalogram (EEG), galvanic skin response (GSR), and electrocardiogram (ECG) recordings from 17 healthy participants during an affective music brain-computer interface training study. Participants listened to 40-second music clips (20s per emotional state) designed to induce specific emotional states across three sessions, with self-reported valence and arousal ratings. The data supports the development and validation of music-based brain-computer interfaces for monitoring and inducing affective states. This is the training session dataset; two additional datasets cover system calibration and online real-time control phases.","owner_user_id":15,"status":"active","github_repo":"nemarDatasets/on002724","concept_doi":"10.82901/nemar.on002724","latest_version_doi":"10.82901/nemar.on002724.v1.0.0","created_at":"2026-06-21 09:00:59","updated_at":"2026-07-10 22:11:33","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: training sessions\",\n  \"description\": \"This dataset contains electroencephalogram (EEG), galvanic skin response (GSR), and electrocardiogram (ECG) recordings from 17 healthy participants during an affective music brain-computer interface training study. Participants listened to 40-second music clips (20s per emotional state) designed to induce specific emotional states across three sessions, with self-reported valence and arousal ratings. The data supports the development and validation of music-based brain-computer interfaces for monitoring and inducing affective states. This is the training session dataset; two additional datasets cover system calibration and online real-time control phases.\",\n  \"methods_description\": \"EEG, GSR, and ECG data were recorded at 1 kHz sampling rate from 17 participants listening to synthetic music clips (40 s duration, with 20s targeting emotional state A and 20s targeting emotional state B per clip). Data collection occurred over 3 sessions on separate days, each containing 4 runs of 18 trials. Participants provided self-reported valence and arousal ratings for each trial. Music clips were generated using the synthetic music generator described in Williams et al. (2017). Methods and analysis are detailed in Daly et al. (2018) and Daly et al. (2015). References: 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). 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. 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.\",\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    },\n    {\n      \"term\": \"brain-computer interface\"\n    },\n    {\n      \"term\": \"emotion recognition\"\n    },\n    {\n      \"term\": \"affective computing\"\n    },\n    {\n      \"term\": \"music perception\"\n    },\n    {\n      \"term\": \"valence\"\n    },\n    {\n      \"term\": \"arousal\"\n    },\n    {\n      \"term\": \"galvanic skin response\"\n    },\n    {\n      \"term\": \"Electrocardiography\",\n      \"subject_scheme\": \"MeSH\",\n      \"value_uri\": \"http://id.nlm.nih.gov/mesh/D004562\"\n    },\n    {\n      \"term\": \"synthetic music\"\n    },\n    {\n      \"term\": \"music-induced emotion\"\n    }\n  ],\n  \"related_identifiers\": [\n    {\n      \"identifier\": \"https://github.com/nemarDatasets/on002724\",\n      \"identifier_type\": \"URL\",\n      \"relation_type\": \"IsDescribedBy\"\n    },\n    {\n      \"identifier\": \"https://nemar.org/dataexplorer/detail?dataset_id=on002724\",\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.2018.3\",\n      \"identifier_type\": \"DOI\",\n      \"relation_type\": \"IsDescribedBy\"\n    },\n    {\n      \"identifier\": \"10.18112/openneuro.ds002724.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    \"9.1 GB (432 files)\"\n  ],\n  \"formats\": [\n    \".edf\",\n    \".gitattributes\",\n    \".json\",\n    \".md\",\n    \".nfsaa93520d9b399143000015bb\",\n    \".tsv\",\n    \".wav\",\n    \".yml\"\n  ],\n  \"source_hash\": \"fdecb0ff7b82cc2e774947b045fd26bd959ada9197b12b333396ee3311dc0681\"\n}","last_activity_at":"2026-06-21 09:00:59","source":"openneuro","source_id":"ds002724","subject_count":10,"modalities":"eeg","age_min":19,"age_max":24,"file_size":9150252438,"total_files":850,"tasks":"run1,run2,run3,run4","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.on002724-blue)](https://doi.org/10.82901/nemar.on002724)\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: offline training to induce target emotional states.\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 train a music brain-computer interface system to induce specific affective states for individual users. For this purpose the participants listened to music clips (40 s) targeting two affective states, as defined by valence and arousal. Data were recorded over 3 sessions (separate days), each containing 4 runs (same day) of 18 trials each. The music clips were generated using a synthetic music generator. The dataset contains the electroencephalogram (EEG), galvanic skin response (GSR) and electrocardiogram (ECG) data from 16 healthy 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: system calibration. doi:\r\n\r\n2.\tEEG data from an affective Music Brain-Computer Interface: online real-time control. doi:\r\n\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 data from 17 subjects, stored using the BIDS format. The sampling rate is 1 kHz and the music listening task corresponding to a music clip is 40 s long (clip duration). During the first 20 s, the music clip targets emotional state A, while for the remaining 20 s the music clip targets emotional state B.\r\n\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\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\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":3,"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":7896712844,"archive_retry_count":0,"records_status":"ready","archive_skip_reason":null,"zarr_errors":96,"zarr_failure_count":96,"zarr_deterministic":1,"zarr_failed_at":"2026-09-06 13:28:01","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":9149979930,"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 09:10:01\",\"metadata_updated_at\":\"2026-06-21 09:10:09\",\"archive_checked_at\":\"2026-06-21 09:15:07\",\"zarr_checked_at\":null,\"records_checked_at\":\"2026-06-21 09:13:20\",\"citations_updated_at\":null,\"channel_montage_checked_at\":\"2026-06-28 23:08:10\",\"hed_checked_at\":\"2026-06-30 04:39:10\",\"data_checked_at\":\"2026-07-29 03:00:20\",\"availability_report_at\":\"2026-07-23 01:12:25\",\"recording_stats_at\":null,\"signal_defaults_at\":\"2026-09-02 11:56:29\"}","participants":10,"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":"8.52 GB","zarr_data_failures":{"count":96,"detail_ref":"zarr/index.json","pending":0,"discovered":96},"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}}