{"dataset":{"id":"57136","dataset_id":"on004505","name":"Real World Table Tennis","description":"This dataset comprises high-density, dual-layer EEG, neck EMG, IMU acceleration, T1-weighted structural MRI, and video recordings from 25 participants performing real-world table tennis. Participants engaged in 60 minutes of table tennis play with a ball machine and a human opponent, along with 10 minutes of standing baseline recording. The dataset supports investigation of neural and physiological correlates of natural, whole-body motor behavior during a dynamic real-world sport task.","owner_user_id":15,"status":"active","github_repo":"nemarDatasets/on004505","concept_doi":"10.82901/nemar.on004505","latest_version_doi":"10.82901/nemar.on004505.v1.0.0","created_at":"2026-06-24 12:31:42","updated_at":"2026-08-19 12:59:15","zenodo_concept_id":null,"is_sandbox":0,"visibility":"public","ezid_status":"public","enrichment_json":"{\n  \"version\": \"2.0\",\n  \"pipeline_stage\": \"validated\",\n  \"title\": \"Real World Table Tennis\",\n  \"description\": \"This dataset comprises high-density, dual-layer EEG, neck EMG, IMU acceleration, T1-weighted structural MRI, and video recordings from 25 participants performing real-world table tennis. Participants engaged in 60 minutes of table tennis play with a ball machine and a human opponent, along with 10 minutes of standing baseline recording. The dataset supports investigation of neural and physiological correlates of natural, whole-body motor behavior during a dynamic real-world sport task.\",\n  \"methods_description\": \"Data were collected using high-density, dual-layer EEG, neck EMG, IMU acceleration sensors, T1 structural MRI, and video recordings. Video data with marked hit events (via Adobe Premiere project files) were collected for 17 of the 25 participants. Processed subject data include ICA decomposition and dipole modeling, with retained components documented in EEG.etc.KeepComponents; raw data are available in the sourcedata folder.\",\n  \"license\": \"CC0\",\n  \"dataset_type\": \"raw\",\n  \"authors\": {\n    \"Amanda Studnicki\": {\n      \"orcid\": \"0000-0003-2835-4452\",\n      \"affiliations\": [\n        {\n          \"name\": \"J. Crayton Pruitt Family Department of Biomedical Engineering, University of Florida, Gainesville, FL 32611, USA\"\n        }\n      ]\n    },\n    \"Daniel P. Ferris\": {\n      \"orcid\": \"0000-0001-6373-6021\",\n      \"affiliations\": [\n        {\n          \"name\": \"J. Crayton Pruitt Family Department of Biomedical Engineering, University of Florida, Gainesville, FL 32611, USA\"\n        }\n      ]\n    }\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\": \"Electromyography\",\n      \"subject_scheme\": \"MeSH\",\n      \"value_uri\": \"http://id.nlm.nih.gov/mesh/D004576\"\n    },\n    {\n      \"term\": \"table tennis\"\n    },\n    {\n      \"term\": \"mobile brain/body imaging\"\n    },\n    {\n      \"term\": \"motor control\"\n    },\n    {\n      \"term\": \"inertial measurement unit\"\n    },\n    {\n      \"term\": \"independent component analysis\"\n    }\n  ],\n  \"related_identifiers\": [\n    {\n      \"identifier\": \"10.3390/s22155867\",\n      \"identifier_type\": \"DOI\",\n      \"relation_type\": \"References\"\n    },\n    {\n      \"identifier\": \"https://github.com/nemarDatasets/on004505\",\n      \"identifier_type\": \"URL\",\n      \"relation_type\": \"IsDescribedBy\"\n    },\n    {\n      \"identifier\": \"10.18112/openneuro.ds004505.v1.0.4\",\n      \"identifier_type\": \"DOI\",\n      \"relation_type\": \"IsDerivedFrom\"\n    },\n    {\n      \"identifier\": \"https://nemar.org/dataset/on004505\",\n      \"identifier_type\": \"URL\",\n      \"relation_type\": \"IsDescribedBy\"\n    }\n  ],\n  \"funding_references\": [\n    {\n      \"funder_name\": \"National Science Foundation\",\n      \"award_number\": \"BCS-1835317\"\n    }\n  ],\n  \"resource_type_specific\": \"Structural MRI Dataset\",\n  \"modalities\": [\n    \"anat\",\n    \"eeg\"\n  ],\n  \"sizes\": [\n    \"561.1 GB (1377 files)\"\n  ],\n  \"formats\": [\n    \".eeg\",\n    \".fdt\",\n    \".json\",\n    \".md\",\n    \".mp4\",\n    \".nii\",\n    \".prproj\",\n    \".set\",\n    \".tsv\",\n    \".txt\",\n    \".vhdr\",\n    \".vmrk\",\n    \".yml\"\n  ],\n  \"source_hash\": \"18d1bd2dcb35caf9d69803c202ae70b2176350d9c5e01734d050cd8a3da84e09\"\n}","last_activity_at":"2026-06-24 12:31:42","source":"openneuro","source_id":"ds004505","subject_count":25,"modalities":"anat,eeg","age_min":18,"age_max":30,"file_size":561092367668,"total_files":1554,"tasks":"TableTennis","metadata_columns_error":null,"staleness_warn_stage":null,"staleness_admin_notified_at":null,"authors":"Amanda Studnicki, Daniel P. Ferris","license":"CC0","readme":"[![DOI](https://img.shields.io/badge/DOI-10.82901%2Fnemar.on004505-blue)](https://doi.org/10.82901/nemar.on004505)\n\nOur dataset contains high-density, dual-layer electroencephalography (EEG), neck electromyography (EMG), inertial measurement unit (IMU) acceleration, T1 structural MR images, and video data from 25 participants playing real-world table tennis. Participants played 60 minutes of table tennis (in total) with a ball machine and a human player, with an additional 10 minutes of standing baseline. For 17 of the participants, we also include video data of all trials. The Adobe Premiere project files (linked to each video) have the timing of hit events marked.\r\n\r\nData in the main subject folders have been processed. We include the ICA decomposition and dipole model in EEG.etc. The components retained in our analyses are shown in EEG.etc.KeepComponents. The raw data can be found in the sourcedata folder.    \r\n\r\nPlease refer to our publication for more details. 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