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Technical validation through event-related potentials and power spectral density analyses confirmed distinct neural responses across stimulus conditions, supporting applications in neural decoding, perception, and cognitive modeling.","owner_user_id":15,"status":"active","github_repo":"nemarDatasets/on005815","concept_doi":"10.82901/nemar.on005815","latest_version_doi":"10.82901/nemar.on005815.v1.0.0","created_at":"2026-06-20 18:13:08","updated_at":"2026-07-10 22:57:11","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 Human EEG\\nDataset for Multisensory Perception and Mental\\nImagery\",\n  \"description\": \"The YOTO (You Only Think Once) dataset is a human electroencephalography resource comprising high-resolution EEG recordings from 20 participants performing multisensory perception and mental imagery tasks. Signals were acquired at 1000 Hz sampling rate during exposure to unimodal (visual and auditory) and multimodal stimuli, with participants providing subjective vividness ratings. Technical validation through event-related potentials and power spectral density analyses confirmed distinct neural responses across stimulus conditions, supporting applications in neural decoding, perception, and cognitive modeling.\",\n  \"methods_description\": \"EEG signals were recorded at 1000 Hz sampling rate from 20 participants performing tasks with visual, auditory, and combined multisensory stimuli. Participants provided self-reported vividness ratings for subjective perceptual assessment. Data validation included event-related potential (ERP) and power spectral density (PSD) analyses. The dataset comprises high-temporal-resolution neural activity recordings designed to investigate the integration of multiple sensory modalities and internal mental representations.\",\n  \"license\": \"CC0\",\n  \"dataset_type\": \"raw\",\n  \"authors\": {\n    \"Yan-Han Chang\": {},\n    \"Hsi-An Chen\": {},\n    \"Min-Jiun Tsai\": {},\n    \"Chun-Lung Tseng\": {},\n    \"Ching-Huei Lo\": {},\n    \"Kuan-Chih Huang\": {},\n    \"Chun-Shu Wei\": {}\n  },\n  \"keywords\": [\n    {\n      \"term\": \"EEG\"\n    },\n    {\n      \"term\": \"multisensory perception\"\n    },\n    {\n      \"term\": \"mental imagery\"\n    },\n    {\n      \"term\": \"event-related potentials\"\n    },\n    {\n      \"term\": \"neural decoding\"\n    },\n    {\n      \"term\": \"multimodal integration\"\n    },\n    {\n      \"term\": \"visual stimuli\"\n    },\n    {\n      \"term\": \"auditory stimuli\"\n    },\n    {\n      \"term\": \"vividness ratings\"\n    },\n    {\n      \"term\": \"cognitive neuroscience\"\n    },\n    {\n      \"term\": \"BIDS\"\n    }\n  ],\n  \"related_identifiers\": [\n    {\n      \"identifier\": \"https://github.com/nemarDatasets/on005815\",\n      \"identifier_type\": \"URL\",\n      \"relation_type\": \"IsDescribedBy\"\n    },\n    {\n      \"identifier\": \"https://nemar.org/dataexplorer/detail?dataset_id=on005815\",\n      \"identifier_type\": \"URL\",\n      \"relation_type\": \"IsDescribedBy\"\n    },\n    {\n      \"identifier\": \"10.18112/openneuro.ds005815\",\n      \"identifier_type\": \"DOI\",\n      \"relation_type\": \"IsVersionOf\"\n    },\n    {\n      \"identifier\": \"10.18112/openneuro.ds005815.v2.0.1\",\n      \"identifier_type\": \"DOI\",\n      \"relation_type\": \"IsDerivedFrom\"\n    }\n  ],\n  \"funding_references\": [\n    {\n      \"funder_name\": \"National Science and Technology Council (NSTC)\",\n      \"award_number\": \"109-2222-E-009-006-MY3\"\n    }\n  ],\n  \"resource_type_general\": \"Dataset\",\n  \"resource_type_specific\": \"EEG Dataset\",\n  \"modalities\": [\n    \"eeg\"\n  ],\n  \"sizes\": [\n    \"10.6 GB (455 files)\"\n  ],\n  \"formats\": [\n    \".eeg\",\n    \".json\",\n    \".mat\",\n    \".md\",\n    \".set\",\n    \".tsv\",\n    \".txt\",\n    \".vhdr\",\n    \".vmrk\",\n    \".yml\"\n  ],\n  \"source_hash\": \"a9f81067b97ccc8b2f67a7a0555d5a145378fd6034968eb1a95824a814e36cda\"\n}","last_activity_at":"2026-06-20 18:13:08","source":"openneuro","source_id":"ds005815","subject_count":20,"modalities":"eeg","age_min":null,"age_max":null,"file_size":10642003297,"total_files":767,"tasks":"rest1,rest2,task","metadata_columns_error":null,"staleness_warn_stage":null,"staleness_admin_notified_at":null,"authors":"Yan-Han Chang, Hsi-An Chen, Min-Jiun Tsai, Chun-Lung Tseng, Ching-Huei Lo, Kuan-Chih Huang, Chun-Shu Wei","license":"CC0","readme":"[![DOI](https://img.shields.io/badge/DOI-10.82901%2Fnemar.on005815-blue)](https://doi.org/10.82901/nemar.on005815)\n\nThe YOTO (You Only Think Once) dataset presents a human electroencephalography (EEG) resource for exploring multisensory perception and mental imagery. The study enrolled 20 participants who performed tasks involving both unimodal and multimodal stimuli. Researchers collected high-resolution EEG signals at a 1000 Hz sampling rate to capture high-temporal-resolution neural activity related to internal mental representations. The protocol incorporated visual, auditory, and combined cues to investigate the integration of multiple sensory modalities, and participants provided self-reported vividness ratings that indicate subjective perceptual strength. Technical validation involved event-related potentials (ERPs) and power spectral density (PSD) analyses, which demonstrated the reliability of the data and confirmed distinct neural responses across stimuli. 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