Motor imagery dataset from Ma et al. 2020

Motor imagery dataset from Ma et al. 2020.

Dataset Overview

• Code: Ma2020
• Paradigm: imagery
• DOI: 10.1038/s41597-020-0535-2
• Subjects: 25
• Sessions per subject: 15
• Events: right_hand=1, right_elbow=2
• Trial interval: [0, 4] s
• 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
• Ground: AFz
• Line frequency: 50.0 Hz
• Impedance threshold: 5 kOhm
• Auxiliary channels: EOG (2 ch, horizontal, vertical), M2

Participants

• Number of subjects: 25
• Health status: healthy
• Age: mean=25.56, min=23, max=29
• Gender distribution: male=18, female=7
• Handedness: {'right': 25}
• BCI experience: naive

Experimental Protocol

• Paradigm: imagery
• Task type: motor_imagery_same_limb
• Number of classes: 2
• Class labels: right_hand, right_elbow
• Trial duration: 4.0 s
• Feedback type: none
• Stimulus type: visual cue
• Stimulus modalities: visual
• Primary modality: visual
• Synchronicity: synchronous
• Mode: offline
• Training/test split: False
• Instructions: Subjects were asked to concentrate on performing the indicated motor imagery task (right hand or right elbow) using kinesthetic, not visual, motor imagery while avoiding any motion during imagination.

HED Event Annotations

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

  right_hand
    ├─ Sensory-event, Experimental-stimulus, Visual-presentation
    └─ Agent-action
       └─ Imagine
          ├─ Move
          └─ Right, Hand

  right_elbow
    ├─ Sensory-event
    └─ Label/right_elbow

Paradigm-Specific Parameters

• Detected paradigm: motor_imagery
• Imagery tasks: right_hand, right_elbow
• Cue duration: 1.0 s
• Imagery duration: 4.0 s

Data Structure

• Trials: 600
• Trials per class: right_hand=300, right_elbow=300
• Blocks per session: 15
• Trials context: 3 days x 5 MI sessions/day = 15 sessions, 40 trials/session (20 hand + 20 elbow)

Signal Processing

• Classifiers: FBCSP+SVM
• Feature extraction: FBCSP
• Frequency bands: alpha=[8.0, 13.0] Hz; beta=[20.0, 25.0] Hz
• Spatial filters: CAR, FBCSP

Cross-Validation

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

BCI Application

• Applications: motor_rehabilitation, prosthetic_control
• Environment: laboratory
• Online feedback: False

Tags

• Pathology: healthy
• Modality: motor
• Type: imagery

Documentation

• DOI: 10.1038/s41597-020-0535-2
• License: CC-BY-4.0
• Investigators: Xuelin Ma, Shuang Qiu, Changde Du, Junfeng Xing, Huiguang He
• Senior author: Huiguang He
• Institution: Chinese Academy of Sciences
• Department: Institute of Automation
• Country: CN
• Repository: Harvard Dataverse
• Data URL: https://doi.org/10.7910/DVN/RBN3XG
• Publication year: 2020
• Funding: National Key Research and Development Plan of China (No. 2017YFB1002502); National Natural Science Foundation of China (No. 61976209); National Natural Science Foundation of China (No. 61906188)
• Ethics approval: Ethics Committee of the Institute of Automation, Chinese Academy of Sciences
• Keywords: motor imagery, EEG, BCI, same limb, hand, elbow

References

X. Ma, S. Qiu, C. Du, J. Xing, and H. He, "Multi-channel EEG recording during motor imagery of different joints from the same limb," Scientific Data, vol. 7, no. 1, p. 191, 2020. DOI: 10.1038/s41597-020-0535-2 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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