eldBETA SSVEP benchmark dataset for elderly population

eldBETA SSVEP benchmark dataset for elderly population.

Dataset Overview

• Code: Liu2022EldBETA
• Paradigm: ssvep
• DOI: 10.1038/s41597-022-01372-9
• Subjects: 100
• Sessions per subject: 7
• Events: 8=1, 9.5=2, 11=3, 8.5=4, 10=5, 11.5=6, 9=7, 10.5=8, 12=9
• Trial interval: [0, 6.0] s
• File format: GDF (BIDS)

Acquisition

• Sampling rate: 1000.0 Hz
• Number of channels: 64
• Channel types: eeg=64
• Montage: standard_1005
• Hardware: Synamps2 (Neuroscan)
• Reference: Cz
• Line frequency: 50.0 Hz
• Impedance threshold: 20 kOhm

Participants

• Number of subjects: 100
• Health status: healthy
• Age: mean=63.17, std=6.05, min=51, max=81
• Gender distribution: male=33, female=67

Experimental Protocol

• Paradigm: ssvep
• Task type: 9-target SSVEP speller
• Number of classes: 9
• Class labels: 8, 9.5, 11, 8.5, 10, 11.5, 9, 10.5, 12
• Trial duration: 5.0 s
• Feedback type: visual
• Stimulus type: JFPM visual flicker
• Stimulus modalities: visual
• Primary modality: visual
• Synchronicity: synchronous
• Mode: online
• Training/test split: False

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

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

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

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

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

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

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

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

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

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] Hz
• Frequency resolution: 0.5 Hz

Data Structure

• Trials: 63
• Blocks per session: 7

Signal Processing

• Classifiers: TDCA, ms-eCCA, ensemble_msTRCA, ensemble_TRCA, Extended_CCA, ITCCA, L1MCCA, FBCCA, CVARS, tMSI, MEC, MSI, CCA
• Feature extraction: TDCA, CCA, FBCCA, TRCA, ms-eCCA, msTRCA, Extended_CCA, ITCCA, L1MCCA, CVARS, tMSI, MEC, MSI
• Frequency bands: bandpass=[6.0, 100.0] Hz
• Spatial filters: TDCA, CCA, TRCA, ms-eCCA, msTRCA, Extended_CCA, ITCCA, L1MCCA, CVARS, MEC, MSI, tMSI

Cross-Validation

• Method: leave-one-block-out
• Folds: 7
• Evaluation type: within_subject

BCI Application

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

Tags

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

Documentation

• DOI: 10.1038/s41597-022-01372-9
• License: CC BY 4.0
• Investigators: Bingchuan Liu, Yijun Wang, Xiaorong Gao, Xiaogang Chen
• Senior author: Xiaogang Chen
• Institution: Tsinghua University
• Department: Department of Biomedical Engineering, School of Medicine, Tsinghua University
• Country: CN
• Repository: Figshare
• Data URL: https://doi.org/10.6084/m9.figshare.18032669
• Publication year: 2022
• Funding: National Natural Science Foundation of China (No. 62171473); Doctoral Brain+X Seed Grant Program of Tsinghua University; Strategic Priority Research Program of Chinese Academy of Sciences (No. XDB32040200)
• Ethics approval: Institutional Review Board of Tsinghua University, No. 20210032
• Keywords: SSVEP, BCI, EEG, elderly, aging, benchmark, JFPM

References

B. Liu, Y. Wang, X. Gao, and X. Chen, "eldBETA: A Large Eldercare-oriented Benchmark Database of SSVEP-BCI for the Aging Population," Scientific Data, vol. 9, p. 252, 2022. DOI: 10.1038/s41597-022-01372-9 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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