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nm000122 NEMAR-native dataset

Chen2017 – Single-flicker online SSVEP BCI dataset

A 32-channel EEG dataset from 12 healthy subjects performing a spatial navigation task using a single-flicker steady-state visual evoked potential (SSVEP) brain-computer interface. The dataset comprises two sessions per subject: a structured training session with calibration data recorded at 2048 Hz, and an online adaptive BCI game session recorded at 512 Hz. Data were acquired using a BioSemi ActiveTwo system with a spatially-coded paradigm where a single 15 Hz flickering stimulus was presented centrally while subjects attended to one of four cardinal direction targets.

EEG
Download zip Issues GitHub

Compute on this dataset

Two routes today, with a third (in-browser one-click submission) landing soon.

  1. NeuroScience Gateway (NSG) portal.

    NSG runs EEGLAB / Brainstorm / MNE pipelines on supercomputing time donated by SDSC. Create an account, point a job at this dataset's S3 prefix (s3://nemar/nm000122), and submit.
    nsgportal.org →

  2. Local processing with nemar-cli.

    Pull the dataset to your machine and run any toolbox locally. Honors the published version pinning. 

    npm install -g nemar-cli
nemar dataset clone nm000122
cd nm000122 && nemar dataset get
  3. Just the files.

    rclone, aria2c, or any HTTPS client works against data.nemar.org/nm000122/ — the manifest carries presigned S3 URLs.

Direct compute access is coming soon. One-click NSG submission from this page is scoped for a follow-up phase. Tracked on nemarOrg/website#6.
DOI 10.82901/nemar.nm000122

![DOI](https://doi.org/10.82901/nemar.nm000122)

Single-flicker online SSVEP BCI dataset

Single-flicker online SSVEP BCI dataset.

Dataset Overview

  • Code: Chen2017SingleFlicker
  • Paradigm: ssvep
  • DOI: 10.1371/journal.pone.0178385
  • Subjects: 12
  • Sessions per subject: 2
  • Events: north=1, east=2, west=3, south=4
  • Trial interval: [0.0, 3.5] s
  • File format: XDF/MAT

Acquisition

  • Sampling rate: 512.0 Hz
  • Number of channels: 32
  • Channel types: eeg=32
  • Montage: biosemi32
  • Hardware: BioSemi ActiveTwo
  • Reference: CMS/DRL
  • Sensor type: active
  • Line frequency: 50.0 Hz
  • Cap manufacturer: BioSemi
  • Electrode material: sintered Ag/AgCl

Participants

  • Number of subjects: 12
  • Health status: healthy
  • Age: mean=23.5, min=19, max=32
  • Gender distribution: male=5, female=7

Experimental Protocol

  • Paradigm: ssvep
  • Task type: spatial navigation
  • Number of classes: 4
  • Class labels: north, east, west, south
  • Study design: Spatial navigation with single 15 Hz flicker
  • Feedback type: visual
  • Stimulus type: single-flicker spatially coded
  • Stimulus modalities: visual
  • Primary modality: visual
  • Synchronicity: synchronous
  • Mode: online
  • Training/test split: True

HED Event Annotations

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

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

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

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

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

Paradigm-Specific Parameters

  • Detected paradigm: ssvep
  • Stimulus frequencies: [15.0] Hz

Signal Processing

  • Classifiers: LDA
  • Feature extraction: CCA
  • Frequency bands: bandpass=[1.0, 80.0] Hz
  • Spatial filters: CCA

Cross-Validation

  • Evaluation type: within_subject

BCI Application

  • Applications: spatial_navigation
  • Environment: lab
  • Online feedback: True

Tags

  • Pathology: healthy
  • Modality: visual
  • Type: perception

Documentation

  • DOI: 10.1371/journal.pone.0178385
  • License: CC BY 4.0
  • Investigators: Jingjing Chen, Dan Zhang, Andreas K. Engel, Qin Gong, Alexander Maye
  • Senior author: Alexander Maye
  • Institution: University Medical Center Hamburg-Eppendorf
  • Department: Department of Neurophysiology and Pathophysiology, University Medical Center Hamburg-Eppendorf
  • Country: DE
  • Repository: Zenodo
  • Data URL: https://zenodo.org/records/580485
  • Publication year: 2017
  • Funding: DFG TRR169/B1/Z2 Crossmodal Learning; Landesforschungsfoerderung Hamburg CROSS FV25
  • Ethics approval: Ethics committee of the medical association, Hamburg
  • Keywords: SSVEP, BCI, spatial navigation, single-flicker, online BCI

References

J. Chen, D. Zhang, A. K. Engel, Q. Gong, and A. Maye, "Application of a single-flicker online SSVEP BCI for spatial navigation," PLoS ONE, vol. 12, no. 5, e0178385, 2017. DOI: 10.1371/journal.pone.0178385 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

Generated by MOABB 1.4.3 (Mother of All BCI Benchmarks) https://github.com/NeuroTechX/moabb

Files

19 top-level entries · 742 MB total