Multi-joint upper-limb MI dataset from Yi et al. 2025

Multi-joint upper-limb MI dataset from Yi et al. 2025.

Dataset Overview

• Code: Yi2025
• Paradigm: imagery
• DOI: 10.1038/s41597-025-05286-0
• Subjects: 18
• Sessions per subject: 1
• Events: hand_open_close=1, wrist_flex_ext=2, wrist_abd_add=3, elbow_pron_sup=4, elbow_flex_ext=5, shoulder_pron_sup=6, shoulder_abd_add=7, shoulder_flex_ext=8
• Trial interval: [0, 4] s
• Runs per session: 8
• File format: CNT

Acquisition

• Sampling rate: 1000.0 Hz
• Number of channels: 62
• Channel types: eeg=62
• 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, TP7, CP5, CP3, CP1, CPz, CP2, CP4, CP6, TP8, 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: Neuroscan SynAmps2
• Reference: left mastoid (M1)
• Line frequency: 50.0 Hz

Participants

• Number of subjects: 18
• Health status: healthy
• Age: min=22, max=27
• Gender distribution: female=10, male=8
• Handedness: right
• BCI experience: naive
• Species: human

Experimental Protocol

• Paradigm: imagery
• Number of classes: 8
• Class labels: hand_open_close, wrist_flex_ext, wrist_abd_add, elbow_pron_sup, elbow_flex_ext, shoulder_pron_sup, shoulder_abd_add, shoulder_flex_ext
• Trial duration: 4.0 s
• Study design: 8-class multi-joint upper-limb MI. 8 blocks of 40 trials (5 per class), 320 total trials per subject.
• Feedback type: none
• Stimulus type: video + text
• Stimulus modalities: visual
• Primary modality: visual
• Synchronicity: cue-based
• Mode: offline

HED Event Annotations

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

  hand_open_close
    ├─ Sensory-event
    └─ Label/hand_open_close

  wrist_flex_ext
    ├─ Sensory-event
    └─ Label/wrist_flex_ext

  wrist_abd_add
    ├─ Sensory-event
    └─ Label/wrist_abd_add

  elbow_pron_sup
    ├─ Sensory-event
    └─ Label/elbow_pron_sup

  elbow_flex_ext
    ├─ Sensory-event
    └─ Label/elbow_flex_ext

  shoulder_pron_sup
    ├─ Sensory-event
    └─ Label/shoulder_pron_sup

  shoulder_abd_add
    ├─ Sensory-event
    └─ Label/shoulder_abd_add

  shoulder_flex_ext
    ├─ Sensory-event
    └─ Label/shoulder_flex_ext

Paradigm-Specific Parameters

• Detected paradigm: motor_imagery
• Imagery tasks: hand_open_close, wrist_flex_ext, wrist_abd_add, elbow_pron_sup, elbow_flex_ext, shoulder_pron_sup, shoulder_abd_add, shoulder_flex_ext
• Cue duration: 2.0 s
• Imagery duration: 4.0 s

Data Structure

• Trials: 320
• Trials per class: hand_open_close=40, wrist_flex_ext=40, wrist_abd_add=40, elbow_pron_sup=40, elbow_flex_ext=40, shoulder_pron_sup=40, shoulder_abd_add=40, shoulder_flex_ext=40
• Blocks per session: 8
• Trials context: 8 blocks x 40 trials (5 per class x 8 classes)

Signal Processing

• Classifiers: ShallowConvNet
• Feature extraction: ERSP
• Frequency bands: alpha=[8.0, 13.0] Hz; beta=[13.0, 30.0] Hz; bandpass=[4.0, 40.0] Hz
• Spatial filters: CAR

Cross-Validation

• Method: 5-fold
• Folds: 5
• Evaluation type: within_subject

BCI Application

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

Tags

• Pathology: Healthy
• Modality: Motor
• Type: Motor Imagery

Documentation

• DOI: 10.1038/s41597-025-05286-0
• License: CC-BY-NC-ND-4.0
• Investigators: Weibo Yi, Jiaming Chen, Dan Wang, Xinkang Hu, Meng Xu, Fangda Li, Shuhan Wu, Jin Qian
• Institution: Beijing University of Technology
• Country: CN
• Data URL: https://figshare.com/articles/dataset/Data/24123303
• Publication year: 2025

References

Yi, W., Chen, J., Wang, D., et al. (2025). A multi-modal dataset of EEG and fNIRS for motor imagery of multi-types of joints from unilateral upper limb. Scientific Data, 12, 953. https://doi.org/10.1038/s41597-025-05286-0

Notes

.. versionadded:: 1.2.0 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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