Motor Imagery dataset from Weibo et al 2014

Motor Imagery dataset from Weibo et al 2014.

Dataset Overview

• Code: Weibo2014
• Paradigm: imagery
• DOI: 10.1371/journal.pone.0114853
• Subjects: 10
• Sessions per subject: 1
• Events: left_hand=1, right_hand=2, hands=3, feet=4, left_hand_right_foot=5, right_hand_left_foot=6, rest=7
• Trial interval: [3, 7] s
• File format: MAT
• Data preprocessed: True

Acquisition

• Sampling rate: 200.0 Hz
• Number of channels: 60
• Channel types: eeg=60, eog=2, misc=2
• Channel names: AF3, AF4, C1, C2, C3, C4, C5, C6, CB1, CB2, CP1, CP2, CP3, CP4, CP5, CP6, CPz, Cz, F1, F2, F3, F4, F5, F6, F7, F8, FC1, FC2, FC3, FC4, FC5, FC6, FCz, FT7, FT8, Fp1, Fp2, Fpz, Fz, HEO, O1, O2, Oz, P1, P2, P3, P4, P5, P6, P7, P8, PO3, PO4, PO5, PO6, PO7, PO8, POz, Pz, T7, T8, TP7, TP8, VEO
• Montage: standard_1005
• Hardware: Neuroscan SynAmps2
• Reference: nose
• Ground: prefrontal lobe
• Sensor type: Ag/AgCl
• Line frequency: 50.0 Hz
• Online filters: {'bandpass': [0.5, 100], 'notch_hz': 50}
• Auxiliary channels: EOG (2 ch, HEO, VEO)

Participants

• Number of subjects: 10
• Health status: healthy
• Age: mean=24.0, min=23.0, max=25.0
• Gender distribution: female=7, male=3
• Handedness: right-handed
• BCI experience: naive
• Species: human

Experimental Protocol

• Paradigm: imagery
• Number of classes: 7
• Class labels: left_hand, right_hand, hands, feet, left_hand_right_foot, right_hand_left_foot, rest
• Trial duration: 8.0 s
• Study design: Simple limb motor imagery (left hand, right hand, feet) and compound limb motor imagery (both hands, left hand combined with right foot, right hand combined with left foot)
• Feedback type: none
• Stimulus type: text cues
• Stimulus modalities: visual
• Primary modality: visual
• Synchronicity: synchronous
• Mode: offline
• Instructions: Participants were asked to perform kinesthetic motor imagery rather than a visual type of imagery while avoiding any muscle movement

HED Event Annotations

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

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

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

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

  feet
    ├─ Sensory-event, Experimental-stimulus, Visual-presentation
    └─ Agent-action
       └─ Imagine, Move, Foot

  left_hand_right_foot
    ├─ Sensory-event, Experimental-stimulus, Visual-presentation
    └─ Agent-action
       ├─ Imagine
       │  ├─ Move
       │  └─ Left, Hand
       └─ Imagine
          ├─ Move
          └─ Right, Foot

  right_hand_left_foot
    ├─ Sensory-event, Experimental-stimulus, Visual-presentation
    └─ Agent-action
       ├─ Imagine
       │  ├─ Move
       │  └─ Right, Hand
       └─ Imagine
          ├─ Move
          └─ Left, Foot

  rest
    ├─ Sensory-event
    ├─ Experimental-stimulus
    ├─ Visual-presentation
    └─ Rest

Paradigm-Specific Parameters

• Detected paradigm: motor_imagery
• Imagery tasks: left_hand, right_hand, feet, both_hands, left_hand_right_foot, right_hand_left_foot
• Cue duration: 1.0 s
• Imagery duration: 4.0 s

Data Structure

• Trials: 560
• Trials context: 8 sections with 60 trials each (10 trials per MI task per section) for 6 MI tasks, plus 1 section with 80 trials for rest state

Preprocessing

• Data state: preprocessed
• Preprocessing applied: True
• Steps: bandpass filtering, downsampling
• Highpass filter: 0.5 Hz
• Lowpass filter: 50.0 Hz
• Bandpass filter: {'low_cutoff_hz': 0.5, 'high_cutoff_hz': 50.0}
• Re-reference: nose
• Downsampled to: 200.0 Hz

Signal Processing

• Feature extraction: Bandpower, ERD, ERS, ERSP, Time-Frequency, AR, DTF, PLV
• Frequency bands: theta=[4.0, 5.0] Hz; alpha=[8.0, 13.0] Hz; beta=[13.0, 30.0] Hz; analyzed=[1.0, 40.0] Hz

BCI Application

• Applications: motor_control
• Environment: laboratory

Tags

• Pathology: Healthy
• Modality: Motor
• Type: Research

Documentation

• DOI: 10.1371/journal.pone.0114853
• License: CC0-1.0
• Investigators: Weibo Yi, Shuang Qiu, Kun Wang, Hongzhi Qi, Lixin Zhang, Peng Zhou, Feng He, Dong Ming
• Senior author: Dong Ming
• Contact: qhz@tju.edu.cn; richardming@tju.edu.cn
• Institution: Tianjin University
• Department: Department of Biomedical Engineering
• Country: CN
• Repository: Harvard Dataverse Database
• Data URL: http://dx.doi.org/10.7910/DVN/27306
• Publication year: 2014
• Funding: National Natural Science Foundation of China (No. 81222021, 61172008, 81171423, 51377120, 31271062); National Key Technology R&D Program of the Ministry of Science and Technology of China (No. 2012BAI34B02); Program for New Century Excellent Talents in University of the Ministry of Education of China (No. NCET-10-0618); Natural Science Foundation of Tianjin (No. 13JCQNJC13900)
• Ethics approval: Ethical committee of Tianjin University
• Keywords: motor imagery, compound limb motor imagery, EEG oscillatory patterns, cognitive process, effective connectivity, ERD, ERS

References

Yi, Weibo, et al. "Evaluation of EEG oscillatory patterns and cognitive process during simple and compound limb motor imagery." PloS one 9.12 (2014). https://doi.org/10.1371/journal.pone.0114853 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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