{"dataset":{"id":"52082","dataset_id":"on003004","name":"Imagined Emotion Study","description":"This high-density EEG dataset captures brain activity during guided emotional imagery across 15 distinct emotion states. Participants listened to voice-guided scenarios designed to evoke emotions with varying valence (positive and negative) while their neural activity was recorded with eyes closed. The dataset includes preprocessed EEG data with artifact correction and high-pass filtering, providing a resource for investigating neural correlates of emotion imagination and brain state dynamics during emotional experiences.","owner_user_id":15,"status":"active","github_repo":"nemarDatasets/on003004","concept_doi":"10.82901/nemar.on003004","latest_version_doi":"10.82901/nemar.on003004.v1.0.0","created_at":"2026-06-21 14:30:55","updated_at":"2026-07-10 22:13:04","zenodo_concept_id":null,"is_sandbox":0,"visibility":"public","ezid_status":"public","enrichment_json":"{\n  \"version\": \"2.0\",\n  \"pipeline_stage\": \"validated\",\n  \"title\": \"Imagined Emotion Study\",\n  \"description\": \"This high-density EEG dataset captures brain activity during guided emotional imagery across 15 distinct emotion states. Participants listened to voice-guided scenarios designed to evoke emotions with varying valence (positive and negative) while their neural activity was recorded with eyes closed. The dataset includes preprocessed EEG data with artifact correction and high-pass filtering, providing a resource for investigating neural correlates of emotion imagination and brain state dynamics during emotional experiences.\",\n  \"methods_description\": \"Participants underwent guided imagery procedures in which voice recordings suggested imagining scenarios associated with 15 target emotions. Participants indicated emotion onset and offset via button press and were instructed to attend to somatosensory sensations to intensify emotional experiences. High-density EEG was acquired during these periods, interspersed with relaxation instructions. Raw data were preprocessed to correct event codes, remove noisy channels, and apply 1-Hz high-pass filtering for ICA decomposition.\",\n  \"license\": \"CC0\",\n  \"dataset_type\": \"raw\",\n  \"authors\": {\n    \"Julie Onton\": {},\n    \"Scott Makeig\": {}\n  },\n  \"keywords\": [\n    {\n      \"term\": \"EEG\"\n    },\n    {\n      \"term\": \"emotion imagery\"\n    },\n    {\n      \"term\": \"guided imagery\"\n    },\n    {\n      \"term\": \"brain state dynamics\"\n    },\n    {\n      \"term\": \"affective neuroscience\"\n    },\n    {\n      \"term\": \"high-density EEG\"\n    }\n  ],\n  \"related_identifiers\": [\n    {\n      \"identifier\": \"10.3389/neuro.09.061.2009\",\n      \"identifier_type\": \"DOI\",\n      \"relation_type\": \"References\"\n    },\n    {\n      \"identifier\": \"10.1109/ACII.2013.160\",\n      \"identifier_type\": \"DOI\",\n      \"relation_type\": \"References\"\n    },\n    {\n      \"identifier\": \"10.1016/j.neuroimage.2022.118873\",\n      \"identifier_type\": \"DOI\",\n      \"relation_type\": \"References\"\n    },\n    {\n      \"identifier\": \"https://github.com/nemarDatasets/on003004\",\n      \"identifier_type\": \"URL\",\n      \"relation_type\": \"IsDescribedBy\"\n    },\n    {\n      \"identifier\": \"https://nemar.org/dataexplorer/detail?dataset_id=on003004\",\n      \"identifier_type\": \"URL\",\n      \"relation_type\": \"IsDescribedBy\"\n    },\n    {\n      \"identifier\": \"10.18112/openneuro.ds003004.v1.1.1\",\n      \"identifier_type\": \"DOI\",\n      \"relation_type\": \"IsDerivedFrom\"\n    }\n  ],\n  \"resource_type_specific\": \"EEG Dataset\",\n  \"modalities\": [\n    \"eeg\"\n  ],\n  \"sizes\": [\n    \"38.7 GB (69 files)\"\n  ],\n  \"formats\": [\n    \".fdt\",\n    \".gitattributes\",\n    \".json\",\n    \".md\",\n    \".set\",\n    \".tsv\",\n    \".yml\"\n  ],\n  \"source_hash\": \"5ec75e2a2c11f73b35ed3cc048295714481248ae15421557a2aa5dd8b99ea349\"\n}","last_activity_at":"2026-06-21 14:30:55","source":"openneuro","source_id":"ds003004","subject_count":34,"modalities":"eeg","age_min":18,"age_max":38,"file_size":38704526222,"total_files":282,"tasks":"ImaginedEmotion","metadata_columns_error":null,"staleness_warn_stage":null,"staleness_admin_notified_at":null,"authors":"Julie Onton, Scott Makeig","license":"CC0","readme":"[![DOI](https://img.shields.io/badge/DOI-10.82901%2Fnemar.on003004-blue)](https://doi.org/10.82901/nemar.on003004)\n\n**PARADIGM:** The study uses the method of guided imagery to induce resting, eyes-closed participants using voice-guided imagination to enter distinct 15 emotion states during acquisition of high-density EEG data. \r\n\r\nDuring the study, participants listen to 15 voice recordings that each suggest imagining a scenario in which they have experienced -- or would experience the named target emotion. Some target emotions have positive valence (e.g., joy, happiness), others negative valence (e.g., sadness, anger). Before and between the 15 emotion imagination periods, participants hear relaxation suggestions ('Now return to a neutral state by ...').\r\n\r\n**PROCEDURE:** When the participant first begins to feel the target emotion, they are asked to indicate this by pressing a handheld button. Participants are asked to continue feeling the emotion as long as possible.  To intensify and lengthen the periods of experienced emotion, participants are asked to interoceptively perceive and attend relevant somatosensory sensations. When the target feeling wanes (typically after 1 and 5 minutes), participants push the button again to leave the emotion imagination period and cue the relaxation instructions.\r\n\r\n**DATA HANDLING:** The raw data have been preprocessed to fix confusing event codes and to remove excessively noisy channels. In addition, a 1-Hz high pass filter  was applied to ready the data for ICA decomposition. Note: Unfortunately, the unfiltered data are no longer available.\r\n\r\n**NOTE:** Sub22 was a repeat subject, hence was removed from the dataset. 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