SSVEP Exo dataset

SSVEP Exo dataset.

Dataset Overview

• Code: Kalunga2016
• Paradigm: ssvep
• DOI: 10.1016/j.neucom.2016.01.007
• Subjects: 12
• Sessions per subject: 1
• Events: 13=2, 17=4, 21=3, rest=1
• Trial interval: [2, 4] s
• File format: fif

Acquisition

• Sampling rate: 256.0 Hz
• Number of channels: 8
• Channel types: eeg=8
• Channel names: Oz, O1, O2, POz, PO3, PO4, PO7, PO8
• Montage: standard_1005
• Hardware: g.tec MobiLab
• Reference: right mastoid
• Sensor type: EEG
• Line frequency: 50.0 Hz

Participants

• Number of subjects: 12
• Health status: healthy
• Species: human

Experimental Protocol

• Paradigm: ssvep
• Number of classes: 4
• Class labels: 13, 17, 21, rest
• Trial duration: 6.0 s
• Study design: SSVEP
• Feedback type: none
• Stimulus type: flickering
• Stimulus modalities: visual
• Primary modality: visual
• Synchronicity: synchronous
• Mode: offline
• Stimulus presentation: device=LED stimuli, frequencies=13 Hz, 17 Hz, 21 Hz, note=No phase synchronization required

HED Event Annotations

Schema: HED 8.4.0 | Browse: https://www.hedtags.org/hed-schema-browser

  13
    ├─ Sensory-event
    ├─ Experimental-stimulus
    ├─ Visual-presentation
    └─ Label/13

  17
    ├─ Sensory-event
    ├─ Experimental-stimulus
    ├─ Visual-presentation
    └─ Label/17

  21
    ├─ Sensory-event
    ├─ Experimental-stimulus
    ├─ Visual-presentation
    └─ Label/21

  rest
    ├─ Experiment-structure
    └─ Rest

Paradigm-Specific Parameters

• Detected paradigm: ssvep
• Stimulus frequencies: [13.0, 17.0, 21.0] Hz
• Number of targets: 3

Data Structure

• Trials: 32 trials per session (8 per visual stimulus, 8 for resting class)
• Trials context: per session

Preprocessing

• Preprocessing applied: False

Signal Processing

• Classifiers: MDRM, CCA
• Feature extraction: Covariance/Riemannian

Cross-Validation

• Method: bootstrap
• Evaluation type: cross_subject, cross_session

BCI Application

• Applications: assistive_robotics
• Environment: laboratory
• Online feedback: False

Tags

• Pathology: Healthy
• Modality: Visual
• Type: Perception

Documentation

• Description: Online SSVEP-based BCI using Riemannian geometry for assistive robotics with shared control scheme
• DOI: 10.1016/j.neucom.2016.01.007
• License: CC-BY-4.0
• Investigators: Emmanuel K. Kalunga, Sylvain Chevallier, Quentin Barthélemy, Karim Djouani, Eric Monacelli, Yskandar Hamam
• Senior author: Sylvain Chevallier
• Institution: Universite de Versailles Saint-Quentin
• Department: Laboratoire d'Ingénierie des Systèmes de Versailles
• Address: 78140 Velizy, France
• Country: FR
• Repository: Zenodo
• Data URL: https://zenodo.org/record/2392979
• Publication year: 2016
• Keywords: Riemannian geometry, Online, Asynchronous, Brain-Computer Interfaces, Steady State Visually Evoked Potentials

References

Emmanuel K. Kalunga, Sylvain Chevallier, Quentin Barthelemy. "Online SSVEP-based BCI using Riemannian Geometry". Neurocomputing, 2016. arXiv report: https://arxiv.org/abs/1501.03227 Appelhoff, 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

Pernet, 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. Scientific Data, 6, 103. https://doi.org/10.1038/s41597-019-0104-8

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