{"dataset":{"id":"269","dataset_id":"nm000236","name":"Dataset of an EEG-based BCI experiment in Virtual Reality using P300","description":"This dataset comprises electroencephalographic recordings from 21 healthy subjects performing a visual P300-based brain-computer interface task in two environments: a personal computer and a virtual reality headset. The study compares BCI performance and user experience across these modalities using a 6×6 matrix speller paradigm with 16-channel EEG recordings at 512 Hz sampling rate. The experiment was conducted at GIPSA-lab in 2018 and includes both physiological and subjective outcome measures.","owner_user_id":19,"status":"active","github_repo":"nemarDatasets/nm000236","concept_doi":"10.82901/nemar.nm000236","latest_version_doi":"10.82901/nemar.nm000236.v1.0.4","created_at":"2026-03-25 18:19:26","updated_at":"2026-08-18 21:20:10","zenodo_concept_id":"20520312","is_sandbox":0,"visibility":"public","ezid_status":"public","enrichment_json":"{\n  \"version\": \"2.0\",\n  \"pipeline_stage\": \"validated\",\n  \"title\": \"Dataset of an EEG-based BCI experiment in Virtual Reality using P300\",\n  \"description\": \"This dataset comprises electroencephalographic recordings from 21 healthy subjects performing a visual P300-based brain-computer interface task in two environments: a personal computer and a virtual reality headset. The study compares BCI performance and user experience across these modalities using a 6×6 matrix speller paradigm with 16-channel EEG recordings at 512 Hz sampling rate. The experiment was conducted at GIPSA-lab in 2018 and includes both physiological and subjective outcome measures.\",\n  \"methods_description\": \"EEG data were acquired from 21 subjects using a g.USBamp amplifier (g.tec) with 16 wet electrodes in a 10-10 montage (reference: right earlobe, ground: AFZ) at 512 Hz sampling rate. Two randomized sessions (PC and VR) were conducted per subject, each consisting of 12 blocks of 5 repetitions. Each repetition involved 12 flashes of groups of 6 symbols in a 6×6 matrix, with each symbol flashing exactly twice per repetition (2 target, 10 non-target flashes). Visual stimulation was delivered via Unity engine on PC and via a passive VRElegiant HMD with Huawei Mate 7 smartphone for VR. Random visual feedback was provided after each repetition.\",\n  \"license\": \"CC-BY-4.0\",\n  \"dataset_type\": \"derivative\",\n  \"authors\": {\n    \"Grégoire Cattan\": {\n      \"orcid\": \"0000-0002-7515-0690\",\n      \"affiliations\": [\n        {\n          \"name\": \"GIPSA-lab, IHMTEK\"\n        }\n      ]\n    },\n    \"Anton Andreev\": {},\n    \"Pedro Luiz Coelho Rodrigues\": {},\n    \"Marco Congedo\": {\n      \"orcid\": \"0000-0003-2196-0409\",\n      \"affiliations\": [\n        {\n          \"name\": \"GIPSA-lab\"\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\": \"P300\"\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\": \"Virtual Reality\"\n    },\n    {\n      \"term\": \"Event-Related Potentials, P300\",\n      \"subject_scheme\": \"MeSH\",\n      \"scheme_uri\": \"https://id.nlm.nih.gov/mesh/\",\n      \"value_uri\": \"http://id.nlm.nih.gov/mesh/D018913\"\n    },\n    {\n      \"term\": \"Visual Perception\",\n      \"subject_scheme\": \"MeSH\",\n      \"value_uri\": \"http://id.nlm.nih.gov/mesh/D014796\"\n    }\n  ],\n  \"related_identifiers\": [\n    {\n      \"identifier\": \"10.5281/zenodo.2605204\",\n      \"identifier_type\": \"DOI\",\n      \"relation_type\": \"References\"\n    },\n    {\n      \"identifier\": \"https://github.com/nemarDatasets/nm000236\",\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/nm000236\",\n      \"identifier_type\": \"URL\",\n      \"relation_type\": \"IsDescribedBy\"\n    }\n  ],\n  \"funding_references\": [\n    {\n      \"funder_name\": \"IHMTEK Company (Interaction Homme-Machine Technologie)\"\n    },\n    {\n      \"funder_name\": \"IHMTEK Company\"\n    }\n  ],\n  \"resource_type_general\": \"Dataset\",\n  \"resource_type_specific\": \"EEG Dataset\",\n  \"modalities\": [\n    \"eeg\"\n  ],\n  \"sizes\": [\n    \"1.0 GB (2563 files)\"\n  ],\n  \"formats\": [\n    \".bdf\",\n    \".json\",\n    \".mat\",\n    \".md\",\n    \".tsv\",\n    \".yaml\",\n    \".yml\"\n  ],\n  \"source_hash\": \"627350cb0f50d778a4223aa0c04525cb5a85ca3bf6f810669043912a7b4f1bd3\"\n}","last_activity_at":"2026-08-16 13:57:44","source":null,"source_id":null,"subject_count":21,"modalities":"eeg","age_min":26.38,"age_max":26.38,"file_size":1047272994,"total_files":15257,"tasks":"p300","metadata_columns_error":null,"staleness_warn_stage":null,"staleness_admin_notified_at":null,"authors":"Grégoire Cattan, Anton Andreev, Pedro Luiz Coelho Rodrigues, Marco Congedo","license":"CC-BY-4.0","readme":"[![DOI](https://img.shields.io/badge/DOI-10.82901%2Fnemar.nm000236-blue)](https://doi.org/10.82901/nemar.nm000236)\n\n# Dataset of an EEG-based BCI experiment in Virtual Reality using P300\n\nDataset of an EEG-based BCI experiment in Virtual Reality using P300.\n\n## Dataset Overview\n\n- **Code**: Cattan2019-VR\n- **Paradigm**: p300\n- **DOI**: https://doi.org/10.5281/zenodo.2605204\n- **Subjects**: 21\n- **Sessions per subject**: 1\n- **Events**: Target=2, NonTarget=1\n- **Trial interval**: [0, 1.0] s\n- **Runs per session**: 60\n- **Session IDs**: PC, VR\n- **File format**: mat, csv\n- **Contributing labs**: GIPSA-lab\n\n## Acquisition\n\n- **Sampling rate**: 512.0 Hz\n- **Number of channels**: 16\n- **Channel types**: eeg=16\n- **Channel names**: Fp1, Fp2, Fc5, Fz, Fc6, T7, Cz, T8, P7, P3, Pz, P4, P8, O1, Oz, O2\n- **Montage**: 10-10\n- **Hardware**: g.USBamp (g.tec, Schiedlberg, Austria)\n- **Software**: OpenVibe\n- **Reference**: right earlobe\n- **Ground**: AFZ\n- **Sensor type**: wet electrodes\n- **Line frequency**: 50.0 Hz\n- **Online filters**: no digital filter applied\n- **Cap manufacturer**: EasyCap\n- **Cap model**: EC20\n\n## Participants\n\n- **Number of subjects**: 21\n- **Health status**: healthy\n- **Age**: mean=26.38, std=5.78, min=19.0, max=44.0\n- **Gender distribution**: male=14, female=7\n- **BCI experience**: varied gaming experience: some played video games occasionally, some played First Person Shooters; varied VR experience from none to repetitive\n\n## Experimental Protocol\n\n- **Paradigm**: p300\n- **Number of classes**: 2\n- **Class labels**: Target, NonTarget\n- **Study design**: randomized session order (PC vs VR); limit eye blinks, head movements and face muscular contractions\n- **Feedback type**: visual\n- **Stimulus type**: flashing white crosses in 6x6 matrix\n- **Stimulus modalities**: visual\n- **Primary modality**: visual\n- **Mode**: offline\n- **Training/test split**: False\n- **Instructions**: focus on a red-squared target symbol while groups of six symbols flash\n- **Stimulus presentation**: description=6x6 matrix of white crosses; groups of 6 symbols flash; each symbol flashes exactly 2 times per repetition, platform=Unity engine exported to PC and VR\n\n## HED Event Annotations\n\nSchema: HED 8.4.0 | Browse: https://www.hedtags.org/hed-schema-browser\n\n```\n  Target\n    ├─ Sensory-event\n    ├─ Experimental-stimulus\n    ├─ Visual-presentation\n    └─ Target\n\n  NonTarget\n    ├─ Sensory-event\n    ├─ Experimental-stimulus\n    ├─ Visual-presentation\n    └─ Non-target\n\n```\n## Paradigm-Specific Parameters\n\n- **Detected paradigm**: p300\n- **Number of targets**: 1\n- **Number of repetitions**: 12\n\n## Data Structure\n\n- **Trials**: {'target': 120, 'non_target': 600}\n- **Trials per class**: target=120, non_target=600\n- **Blocks per session**: 12\n- **Trials context**: per session: 12 blocks × 5 repetitions × 12 flashes per repetition (2 target, 10 non-target)\n\n## Preprocessing\n\n- **Data state**: raw EEG with software tagging via USB (note: tagging introduces jitter and latency - mean 38ms in PC, 117ms in VR)\n- **Preprocessing applied**: False\n- **Notes**: mean tagging latency: ~38 ms in PC, ~117 ms in VR due to different hardware/software setup; these latencies should be used to correct ERPs\n\n## Signal Processing\n\n- **Classifiers**: xDAWN, Riemannian\n- **Feature extraction**: Covariance/Riemannian, xDAWN\n\n## Cross-Validation\n\n- **Evaluation type**: cross_session\n\n## BCI Application\n\n- **Applications**: speller\n- **Environment**: PC and Virtual Reality (VRElegiant HMD with Huawei Ascend Mate 7 smartphone)\n- **Online feedback**: False\n\n## Tags\n\n- **Pathology**: Healthy\n- **Modality**: Visual\n- **Type**: Perception\n\n## Documentation\n\n- **Description**: EEG recordings of 21 subjects doing a visual P300 experiment on PC and VR to compare BCI performance and user experience\n- **DOI**: 10.5281/zenodo.2605204\n- **Associated paper DOI**: hal-02078533v3\n- **License**: CC-BY-4.0\n- **Investigators**: Grégoire Cattan, Anton Andreev, Pedro Luiz Coelho Rodrigues, Marco Congedo\n- **Senior author**: Marco Congedo\n- **Institution**: GIPSA-lab\n- **Department**: GIPSA-lab, CNRS, University Grenoble-Alpes, Grenoble INP\n- **Address**: GIPSA-lab, 11 rue des Mathématiques, Grenoble Campus BP46, F-38402, France\n- **Country**: FR\n- **Repository**: Zenodo\n- **Data URL**: https://doi.org/10.5281/zenodo.2605204\n- **Publication year**: 2019\n- **Funding**: IHMTEK Company (Interaction Homme-Machine Technologie)\n- **Ethics approval**: Ethical Committee of the University of Grenoble Alpes (Comité d'Ethique pour la Recherche Non-Interventionnelle)\n- **Acknowledgements**: promoted by the IHMTEK Company\n- **Keywords**: Electroencephalography (EEG), P300, Brain-Computer Interface (BCI), Virtual Reality (VR), experiment\n\n## Abstract\n\nDataset contains electroencephalographic recordings on 21 subjects doing a visual P300 experiment on PC and VR. The visual P300 is an event-related potential elicited by a visual stimulation, peaking 240–600 ms after stimulus onset. The experiment compares P300-based BCI on PC vs VR headset (passive HMD with smartphone) concerning physiological, subjective and performance aspects. EEG recorded with 16 electrodes. Experiment conducted at GIPSA-lab in 2018.\n\n## Methodology\n\nTwo randomized sessions (PC and VR). Each session: 12 blocks of 5 repetitions. Each repetition: 12 flashes of groups of 6 symbols, ensuring each symbol flashes exactly 2 times. Target flashes twice per repetition (2 target flashes), non-target flashes 10 times. Random feedback given after each repetition (70% expected accuracy). P300 interface: 6x6 matrix of white flashing crosses with red-squared target. VR used passive HMD (VRElegiant) with Huawei Mate 7 smartphone. IMU deactivated to prevent drift. Unity engine used for identical visual stimulation across PC and VR.\n\n## References\n\nG. Cattan, A. Andreev, P. L. C. Rodrigues, and M. Congedo (2019). Dataset of an EEG-based BCI experiment in Virtual Reality and on a Personal Computer. Research Report, GIPSA-lab; IHMTEK. https://doi.org/10.5281/zenodo.2605204\n\n.. versionadded:: 0.5.0\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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