{"dataset":{"id":"126","dataset_id":"nm000123","name":"Kalunga2016 – SSVEP Exo dataset","description":"This dataset contains SSVEP (Steady-State Visually Evoked Potential) EEG recordings from 12 healthy subjects seated in an exoskeleton-equipped wheelchair, collected as part of E. Kalunga's PhD research at the University of Versailles. Subjects focused on LED panels flickering at 13, 17, and 21 Hz, or on a fixation point (reject/rest class), across 1–5 sessions per subject. The dataset was designed for developing online SSVEP-based brain-computer interface (BCI) systems using Riemannian geometry, with applications toward assistive robotics.","owner_user_id":19,"status":"active","github_repo":"nemarDatasets/nm000123","concept_doi":"10.82901/nemar.nm000123","latest_version_doi":"10.82901/nemar.nm000123.v1.0.1","created_at":"2026-03-06 21:18:59","updated_at":"2026-08-18 22:40:57","zenodo_concept_id":null,"is_sandbox":0,"visibility":"public","ezid_status":"public","enrichment_json":"{\n  \"version\": \"2.0\",\n  \"pipeline_stage\": \"validated\",\n  \"title\": \"Kalunga2016 – SSVEP Exo dataset\",\n  \"description\": \"This dataset contains SSVEP (Steady-State Visually Evoked Potential) EEG recordings from 12 healthy subjects seated in an exoskeleton-equipped wheelchair, collected as part of E. Kalunga's PhD research at the University of Versailles. Subjects focused on LED panels flickering at 13, 17, and 21 Hz, or on a fixation point (reject/rest class), across 1–5 sessions per subject. The dataset was designed for developing online SSVEP-based brain-computer interface (BCI) systems using Riemannian geometry, with applications toward assistive robotics.\",\n  \"methods_description\": \"EEG data were recorded with an 8-channel g.tec MobiLab amplifier at 256 Hz sampling rate, using a standard_1005 montage referenced to the right mastoid. Subjects viewed a panel of LEDs flickering at 13, 17, or 21 Hz, or focused on a fixation point (reject class), in trials lasting 5 seconds with a 3-second pause between trials. Each session consisted of 32 trials (8 per class).\",\n  \"license\": \"CC-BY-4.0\",\n  \"dataset_type\": \"derivative\",\n  \"authors\": {\n    \"Emmanuel K. Kalunga\": {},\n    \"Sylvain Chevallier\": {\n      \"orcid\": \"0000-0003-3027-8241\"\n    },\n    \"Quentin Barthélemy\": {},\n    \"Karim Djouani\": {},\n    \"Eric Monacelli\": {},\n    \"Yskandar Hamam\": {}\n  },\n  \"keywords\": [\n    {\n      \"term\": \"Steady-State Evoked Potentials, Visual\"\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\": \"EEG\"\n    },\n    {\n      \"term\": \"Riemannian geometry\"\n    },\n    {\n      \"term\": \"Online BCI\"\n    },\n    {\n      \"term\": \"Asynchronous\"\n    },\n    {\n      \"term\": \"Assistive robotics\"\n    }\n  ],\n  \"related_identifiers\": [\n    {\n      \"identifier\": \"10.1016/j.neucom.2016.01.007\",\n      \"identifier_type\": \"DOI\",\n      \"relation_type\": \"IsDerivedFrom\"\n    },\n    {\n      \"identifier\": \"https://github.com/nemarDatasets/nm000123\",\n      \"identifier_type\": \"URL\",\n      \"relation_type\": \"IsDescribedBy\"\n    },\n    {\n      \"identifier\": \"10.21105/joss.01896\",\n      \"identifier_type\": \"DOI\",\n      \"relation_type\": \"References\"\n    },\n    {\n      \"identifier\": \"10.1038/s41597-019-0104-8\",\n      \"identifier_type\": \"DOI\",\n      \"relation_type\": \"References\"\n    },\n    {\n      \"identifier\": \"https://nemar.org/dataset/nm000123\",\n      \"identifier_type\": \"URL\",\n      \"relation_type\": \"IsDescribedBy\"\n    }\n  ],\n  \"resource_type_general\": \"Dataset\",\n  \"resource_type_specific\": \"EEG Dataset\",\n  \"modalities\": [\n    \"eeg\"\n  ],\n  \"sizes\": [\n    \"167.7 MB (62 files)\"\n  ],\n  \"formats\": [\n    \".fif\",\n    \".html\",\n    \".json\",\n    \".md\",\n    \".set\",\n    \".tsv\",\n    \".yaml\",\n    \".yml\"\n  ],\n  \"source_hash\": \"22c1b9a7675642bd92bf400f88cb69d09f478dd9f1be28947b70f7a98bdd95f1\"\n}","last_activity_at":"2026-08-16 13:24:35","source":null,"source_id":null,"subject_count":12,"modalities":"eeg","age_min":null,"age_max":null,"file_size":167846502,"total_files":247,"tasks":"ssvep","metadata_columns_error":null,"staleness_warn_stage":null,"staleness_admin_notified_at":null,"authors":"Emmanuel K. Kalunga, Sylvain Chevallier, Quentin Barthélemy, Karim Djouani, Eric Monacelli, Yskandar Hamam","license":"CC-BY-4.0","readme":"[![DOI](https://img.shields.io/badge/DOI-10.82901%2Fnemar.nm000123-blue)](https://doi.org/10.82901/nemar.nm000123)\n\n# SSVEP Exo dataset\n\nSSVEP Exo dataset.\n\n## Dataset Overview\n\n- **Code**: Kalunga2016\n- **Paradigm**: ssvep\n- **DOI**: 10.1016/j.neucom.2016.01.007\n- **Subjects**: 12\n- **Sessions per subject**: 1\n- **Events**: 13=2, 17=4, 21=3, rest=1\n- **Trial interval**: [2, 4] s\n- **File format**: fif\n\n## Acquisition\n\n- **Sampling rate**: 256.0 Hz\n- **Number of channels**: 8\n- **Channel types**: eeg=8\n- **Channel names**: Oz, O1, O2, POz, PO3, PO4, PO7, PO8\n- **Montage**: standard_1005\n- **Hardware**: g.tec MobiLab\n- **Reference**: right mastoid\n- **Sensor type**: EEG\n- **Line frequency**: 50.0 Hz\n\n## Participants\n\n- **Number of subjects**: 12\n- **Health status**: healthy\n- **Species**: human\n\n## Experimental Protocol\n\n- **Paradigm**: ssvep\n- **Number of classes**: 4\n- **Class labels**: 13, 17, 21, rest\n- **Trial duration**: 6.0 s\n- **Study design**: SSVEP\n- **Feedback type**: none\n- **Stimulus type**: flickering\n- **Stimulus modalities**: visual\n- **Primary modality**: visual\n- **Synchronicity**: synchronous\n- **Mode**: offline\n- **Stimulus presentation**: device=LED stimuli, frequencies=13 Hz, 17 Hz, 21 Hz, note=No phase synchronization required\n\n## HED Event Annotations\n\nSchema: HED 8.4.0 | Browse: https://www.hedtags.org/hed-schema-browser\n\n```\n  13\n    ├─ Sensory-event\n    ├─ Experimental-stimulus\n    ├─ Visual-presentation\n    └─ Label/13\n\n  17\n    ├─ Sensory-event\n    ├─ Experimental-stimulus\n    ├─ Visual-presentation\n    └─ Label/17\n\n  21\n    ├─ Sensory-event\n    ├─ Experimental-stimulus\n    ├─ Visual-presentation\n    └─ Label/21\n\n  rest\n    ├─ Experiment-structure\n    └─ Rest\n\n```\n## Paradigm-Specific Parameters\n\n- **Detected paradigm**: ssvep\n- **Stimulus frequencies**: [13.0, 17.0, 21.0] Hz\n- **Number of targets**: 3\n\n## Data Structure\n\n- **Trials**: 32 trials per session (8 per visual stimulus, 8 for resting class)\n- **Trials context**: per session\n\n## Preprocessing\n\n- **Preprocessing applied**: False\n\n## Signal Processing\n\n- **Classifiers**: MDRM, CCA\n- **Feature extraction**: Covariance/Riemannian\n\n## Cross-Validation\n\n- **Method**: bootstrap\n- **Evaluation type**: cross_subject, cross_session\n\n## BCI Application\n\n- **Applications**: assistive_robotics\n- **Environment**: laboratory\n- **Online feedback**: False\n\n## Tags\n\n- **Pathology**: Healthy\n- **Modality**: Visual\n- **Type**: Perception\n\n## Documentation\n\n- **Description**: Online SSVEP-based BCI using Riemannian geometry for assistive robotics with shared control scheme\n- **DOI**: 10.1016/j.neucom.2016.01.007\n- **License**: CC-BY-4.0\n- **Investigators**: Emmanuel K. Kalunga, Sylvain Chevallier, Quentin Barthélemy, Karim Djouani, Eric Monacelli, Yskandar Hamam\n- **Senior author**: Sylvain Chevallier\n- **Institution**: Universite de Versailles Saint-Quentin\n- **Department**: Laboratoire d'Ingénierie des Systèmes de Versailles\n- **Address**: 78140 Velizy, France\n- **Country**: FR\n- **Repository**: Zenodo\n- **Data URL**: https://zenodo.org/record/2392979\n- **Publication year**: 2016\n- **Keywords**: Riemannian geometry, Online, Asynchronous, Brain-Computer Interfaces, Steady State Visually Evoked Potentials\n\n## References\n\nEmmanuel K. Kalunga, Sylvain Chevallier, Quentin Barthelemy. \"Online SSVEP-based BCI using Riemannian Geometry\". Neurocomputing, 2016. arXiv report: https://arxiv.org/abs/1501.03227\nAppelhoff, S., Sanderson, M., Brooks, T., Vliet, M., Quentin, R., Holdgraf, C., Chaumon, M., Mikulan, E., Tavabi, K., Hochenberger, R., Welke, D., Brunner, C., Rockhill, A., Larson, E., Gramfort, A. and Jas, M. (2019). MNE-BIDS: Organizing electrophysiological data into the BIDS format and facilitating their analysis. Journal of Open Source Software 4: (1896). https://doi.org/10.21105/joss.01896\n\nPernet, C. R., Appelhoff, S., Gorgolewski, K. J., Flandin, G., Phillips, C., Delorme, A., Oostenveld, R. (2019). EEG-BIDS, an extension to the brain imaging data structure for electroencephalography. 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