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nm000124 NEMAR-native dataset

Han2024 – SSVEP fatigue dataset with two frequency paradigms

A 64-channel EEG dataset from 24 healthy subjects performing steady-state visually evoked potential (SSVEP) brain-computer interface tasks across two frequency paradigms (8.0-15.5 Hz and 25.5-33.0 Hz). The dataset includes training and fatigue-state sessions with 32 visual targets encoded using joint frequency-phase modulation, designed to investigate the effects of mental fatigue on SSVEP-BCI performance and the efficacy of dynamic stopping strategies.

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/nm000124), 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 nm000124
cd nm000124 && nemar dataset get
  3. Just the files.

    rclone, aria2c, or any HTTPS client works against data.nemar.org/nm000124/ — 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.nm000124

![DOI](https://doi.org/10.82901/nemar.nm000124)

SSVEP fatigue dataset with two frequency paradigms

SSVEP fatigue dataset with two frequency paradigms.

Dataset Overview

  • Code: Han2024Fatigue
  • Paradigm: ssvep
  • DOI: 10.1109/TNSRE.2024.3380635
  • Subjects: 24
  • Sessions per subject: 2
  • Events: 8=1, 8.5=2, 9=3, 9.5=4, 10=5, 10.5=6, 11=7, 11.5=8, 12=9, 12.5=10, 13=11, 13.5=12, 14=13, 14.5=14, 15=15, 15.5=16, 25.5=17, 26=18, 26.5=19, 27=20, 27.5=21, 28=22, 28.5=23, 29=24, 29.5=25, 30=26, 30.5=27, 31=28, 31.5=29, 32=30, 32.5=31, 33=32
  • Trial interval: [0.14, 2.14] s
  • File format: MAT

Acquisition

  • Sampling rate: 1000.0 Hz
  • Number of channels: 64
  • Channel types: eeg=64
  • Channel names: Fp1, Fpz, Fp2, AF3, AF4, F7, F5, F3, F1, Fz, F2, F4, F6, F8, FT7, FC5, FC3, FC1, FCz, FC2, FC4, FC6, FT8, T7, C5, C3, C1, Cz, C2, C4, C6, T8, M1, TP7, CP5, CP3, CP1, CPz, CP2, CP4, CP6, TP8, M2, P7, P5, P3, P1, Pz, P2, P4, P6, P8, PO7, PO5, PO3, POz, PO4, PO6, PO8, CB1, O1, Oz, O2, CB2
  • Montage: standard_1005
  • Hardware: Synamps2 (Neuroscan)
  • Reference: Cz
  • Ground: midway between Fz and FPz
  • Line frequency: 50.0 Hz
  • Online filters: {'bandpass_hz': [0.15, 200.0]}
  • Impedance threshold: 10 kOhm

Participants

  • Number of subjects: 24
  • Health status: healthy
  • Age: min=18, max=26
  • Gender distribution: male=12, female=12

Experimental Protocol

  • Paradigm: ssvep
  • Task type: gaze-shifting
  • Number of classes: 32
  • Class labels: 8, 8.5, 9, 9.5, 10, 10.5, 11, 11.5, 12, 12.5, 13, 13.5, 14, 14.5, 15, 15.5, 25.5, 26, 26.5, 27, 27.5, 28, 28.5, 29, 29.5, 30, 30.5, 31, 31.5, 32, 32.5, 33
  • Trial duration: 2.0 s
  • Feedback type: none
  • Stimulus type: JFPM visual flicker
  • Stimulus modalities: visual
  • Primary modality: visual
  • Synchronicity: synchronous
  • Mode: offline
  • Training/test split: True

HED Event Annotations

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

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

  8.5
    ├─ Sensory-event
    ├─ Experimental-stimulus
    ├─ Visual-presentation
    └─ Label/8_5

  9
    ├─ Sensory-event
    ├─ Experimental-stimulus
    ├─ Visual-presentation
    └─ Label/9

  9.5
    ├─ Sensory-event
    ├─ Experimental-stimulus
    ├─ Visual-presentation
    └─ Label/9_5

  10
    ├─ Sensory-event
    ├─ Experimental-stimulus
    ├─ Visual-presentation
    └─ Label/10

  10.5
    ├─ Sensory-event
    ├─ Experimental-stimulus
    ├─ Visual-presentation
    └─ Label/10_5

  11
    ├─ Sensory-event
    ├─ Experimental-stimulus
    ├─ Visual-presentation
    └─ Label/11

  11.5
    ├─ Sensory-event
    ├─ Experimental-stimulus
    ├─ Visual-presentation
    └─ Label/11_5

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

  12.5
    ├─ Sensory-event
    ├─ Experimental-stimulus
    ├─ Visual-presentation
    └─ Label/12_5

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

  13.5
    ├─ Sensory-event
    ├─ Experimental-stimulus
    ├─ Visual-presentation
    └─ Label/13_5

  14
    ├─ Sensory-event
    ├─ Experimental-stimulus
    ├─ Visual-presentation
    └─ Label/14

  14.5
    ├─ Sensory-event
    ├─ Experimental-stimulus
    ├─ Visual-presentation
    └─ Label/14_5

  15
    ├─ Sensory-event
    ├─ Experimental-stimulus
    ├─ Visual-presentation
    └─ Label/15

  15.5
    ├─ Sensory-event
    ├─ Experimental-stimulus
    ├─ Visual-presentation
    └─ Label/15_5

  25.5
    ├─ Sensory-event
    ├─ Experimental-stimulus
    ├─ Visual-presentation
    └─ Label/25_5

  26
    ├─ Sensory-event
    ├─ Experimental-stimulus
    ├─ Visual-presentation
    └─ Label/26

  26.5
    ├─ Sensory-event
    ├─ Experimental-stimulus
    ├─ Visual-presentation
    └─ Label/26_5

  27
    ├─ Sensory-event
    ├─ Experimental-stimulus
    ├─ Visual-presentation
    └─ Label/27

  27.5
    ├─ Sensory-event
    ├─ Experimental-stimulus
    ├─ Visual-presentation
    └─ Label/27_5

  28
    ├─ Sensory-event
    ├─ Experimental-stimulus
    ├─ Visual-presentation
    └─ Label/28

  28.5
    ├─ Sensory-event
    ├─ Experimental-stimulus
    ├─ Visual-presentation
    └─ Label/28_5

  29
    ├─ Sensory-event
    ├─ Experimental-stimulus
    ├─ Visual-presentation
    └─ Label/29

  29.5
    ├─ Sensory-event
    ├─ Experimental-stimulus
    ├─ Visual-presentation
    └─ Label/29_5

  30
    ├─ Sensory-event
    ├─ Experimental-stimulus
    ├─ Visual-presentation
    └─ Label/30

  30.5
    ├─ Sensory-event
    ├─ Experimental-stimulus
    ├─ Visual-presentation
    └─ Label/30_5

  31
    ├─ Sensory-event
    ├─ Experimental-stimulus
    ├─ Visual-presentation
    └─ Label/31

  31.5
    ├─ Sensory-event
    ├─ Experimental-stimulus
    ├─ Visual-presentation
    └─ Label/31_5

  32
    ├─ Sensory-event
    ├─ Experimental-stimulus
    ├─ Visual-presentation
    └─ Label/32

  32.5
    ├─ Sensory-event
    ├─ Experimental-stimulus
    ├─ Visual-presentation
    └─ Label/32_5

  33
    ├─ Sensory-event
    ├─ Experimental-stimulus
    ├─ Visual-presentation
    └─ Label/33

Paradigm-Specific Parameters

  • Detected paradigm: ssvep
  • Stimulus frequencies: [8.0, 8.5, 9.0, 9.5, 10.0, 10.5, 11.0, 11.5, 12.0, 12.5, 13.0, 13.5, 14.0, 14.5, 15.0, 15.5, 25.5, 26.0, 26.5, 27.0, 27.5, 28.0, 28.5, 29.0, 29.5, 30.0, 30.5, 31.0, 31.5, 32.0, 32.5, 33.0] Hz
  • Frequency resolution: 0.5 Hz

Data Structure

  • Trials: 960 per frequency band (16 targets x 60 blocks)
  • Blocks per session: 60
  • Trials context: 6 training + 24 fatigue blocks per frequency condition

Preprocessing

  • Data state: epoched

Signal Processing

  • Classifiers: TRCA
  • Spatial filters: TRCA

BCI Application

  • Environment: lab
  • Online feedback: False

Tags

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

Documentation

  • DOI: 10.1109/TNSRE.2024.3380635
  • License: CC BY 4.0
  • Investigators: Yuheng Han, Yufeng Ke, Ruiyan Wang, Tao Wang, Dong Ming
  • Senior author: Dong Ming
  • Institution: Tianjin University
  • Department: Academy of Medical Engineering and Translational Medicine, Tianjin University
  • Country: CN
  • Repository: Zenodo
  • Data URL: https://zenodo.org/records/10507229
  • Publication year: 2024
  • Funding: National Key Research and Development Program of China (Grant 2021YFF1200603); National Natural Science Foundation of China (Grants 62276184, 61806141)
  • Ethics approval: Research Ethics Committee of Tianjin University
  • Keywords: SSVEP, BCI, fatigue, dynamic stopping, EEG

References

Y. Han, Y. Ke, R. Wang, T. Wang, and D. Ming, "Enhancing SSVEP-BCI Performance Under Fatigue State Using Dynamic Stopping Strategy," IEEE Trans. Neural Syst. Rehab. Eng., vol. 32, pp. 1407-1415, 2024. DOI: 10.1109/TNSRE.2024.3380635 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

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