NEMAR

Login is moving

Authentication for nemar.org is migrating from the legacy system to the new Cloudflare-backed identity. Until that ships, sign in via the CLI: 

npm install -g nemar-cli
nemar login
nm000125 NEMAR-native dataset

Lee2021 – SSVEP paradigm of the Mobile BCI dataset

This dataset comprises steady-state visually evoked potential (SSVEP) recordings from 23 healthy participants performing a brain-computer interface (BCI) task during various locomotor states (standing, walking, running). The study employed a three-class visual flickering stimulus paradigm at frequencies of 5.45, 8.57, and 12.0 Hz, with 73-channel EEG recordings sampled at 100 Hz across 4 sessions per subject. This mobile BCI dataset enables investigation of SSVEP-based neural decoding under naturalistic movement conditions.

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/nm000125), 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 nm000125
cd nm000125 && nemar dataset get
  3. Just the files.

    rclone, aria2c, or any HTTPS client works against data.nemar.org/nm000125/ — 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.nm000125

![DOI](https://doi.org/10.82901/nemar.nm000125)

SSVEP paradigm of the Mobile BCI dataset

SSVEP paradigm of the Mobile BCI dataset.

Dataset Overview

  • Code: Lee2021Mobile-SSVEP
  • Paradigm: ssvep
  • DOI: 10.1038/s41597-021-01094-4
  • Subjects: 23
  • Sessions per subject: 4
  • Events: 5.45=11, 8.57=12, 12.0=13
  • Trial interval: [0, 5] s
  • File format: BrainVision

Acquisition

  • Sampling rate: 100.0 Hz
  • Number of channels: 73
  • Channel types: eeg=73
  • Montage: standard_1005
  • Hardware: BrainAmp (Brain Product GmbH)
  • Reference: FCz
  • Ground: Fpz
  • Sensor type: Ag/AgCl
  • Line frequency: 60.0 Hz
  • Impedance threshold: 50 kOhm
  • Electrode material: Ag/AgCl
  • Auxiliary channels: EOG (4 ch, vertical, horizontal)

Participants

  • Number of subjects: 23
  • Health status: healthy
  • Age: mean=24.5, std=2.9, min=19, max=32
  • Gender distribution: male=13, female=10

Experimental Protocol

  • Paradigm: ssvep
  • Number of classes: 3
  • Class labels: 5.45, 8.57, 12.0
  • Trial duration: 5.0 s
  • Study design: BCI during motion (standing/walking/running)
  • Stimulus type: visual flicker
  • Stimulus modalities: visual
  • Primary modality: visual
  • Synchronicity: synchronous
  • Mode: offline

HED Event Annotations

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

  5.45
    ├─ Sensory-event
    ├─ Experimental-stimulus
    ├─ Visual-presentation
    └─ Label/5_45

  8.57
    ├─ Sensory-event
    ├─ Experimental-stimulus
    ├─ Visual-presentation
    └─ Label/8_57

  12.0
    ├─ Sensory-event
    ├─ Experimental-stimulus
    ├─ Visual-presentation
    └─ Label/12_0

Signal Processing

  • Classifiers: rLDA, CCA
  • Feature extraction: powerovertime_intervals, CCA
  • Frequency bands: delta=[0.5, 3.5] Hz; theta=[3.5, 7.5] Hz; alpha=[7.5, 12.5] Hz; beta=[12.5, 30.0] Hz

Cross-Validation

  • Method: holdout
  • Evaluation type: within_subject

BCI Application

  • Applications: mobile_BCI
  • Environment: treadmill

Tags

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

Documentation

  • DOI: 10.1038/s41597-021-01094-4
  • License: CC BY 4.0
  • Investigators: Young-Eun Lee, Gi-Hwan Shin, Minji Lee, Seong-Whan Lee
  • Senior author: Seong-Whan Lee
  • Institution: Korea University
  • Country: KR
  • Repository: OSF
  • Data URL: https://osf.io/r7s9b/
  • Publication year: 2021
  • Funding: IITP No. 2017-0-00451; IITP No. 2015-0-00185; IITP No. 2019-0-00079
  • Ethics approval: Institutional Review Board of Korea University, KUIRB-2019-0194-01
  • Keywords: SSVEP, ERP, mobile BCI, ear-EEG, locomotion

References

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

30 top-level entries · 1.31 GB total