Classical motor imagery dataset with left hand, right hand, and rest

Classical motor imagery dataset with left hand, right hand, and rest.

Dataset Overview

• Code: Kaya2018
• Paradigm: imagery
• DOI: 10.1038/sdata.2018.211
• Subjects: 7
• Sessions per subject: 1
• Events: lefthand=1, righthand=2, passive=3
• Trial interval: [0, 1] s
• File format: MAT

Acquisition

• Sampling rate: 200.0 Hz
• Number of channels: 19
• Channel types: eeg=19
• Channel names: Fp1, Fp2, F3, F4, C3, C4, P3, P4, O1, O2, F7, F8, T3, T4, T5, T6, Fz, Cz, Pz
• Montage: standard_1020
• Hardware: Nihon Kohden EEG-1200
• Reference: System 0V (0.55*(C3+C4))
• Ground: A1, A2 (earlobes)
• Line frequency: 50.0 Hz

Participants

• Number of subjects: 7
• Health status: healthy
• Age: min=20, max=35
• Gender distribution: male=5, female=2

Experimental Protocol

• Paradigm: imagery
• Task type: leftrighthand
• Number of classes: 3
• Class labels: lefthand, righthand, passive
• Trial duration: 1.0 s
• Study design: Classical left/right hand motor imagery with passive rest
• Feedback type: none
• Stimulus type: visual arrow cue
• Stimulus modalities: visual
• Primary modality: visual
• Synchronicity: synchronous
• Mode: offline

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

  passive
    ├─ Sensory-event
    └─ Label/passive

Paradigm-Specific Parameters

• Detected paradigm: motor_imagery
• Imagery tasks: lefthand, righthand, passive
• Cue duration: 1.0 s

Data Structure

• Trials context: Variable number of trials per session; 1s cue + 1.5-2.5s ITI

Preprocessing

• Data state: raw

Signal Processing

• Classifiers: SVM
• Feature extraction: fouriertransformamplitudes
• Frequency bands: low_pass=[0.0, 5.0] Hz

Cross-Validation

• Method: repeatedrandomsplit
• Folds: 5
• Evaluation type: within_subject

BCI Application

• Environment: lab
• Online feedback: False

Tags

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

Documentation

• DOI: 10.1038/sdata.2018.211
• License: CC-BY-4.0
• Investigators: Murat Kaya, Mustafa Kemal Binli, Erkan Ozbay, Hilmi Yanar, Yuriy Mishchenko
• Senior author: Yuriy Mishchenko
• Institution: Mersin University
• Country: TR
• Repository: Figshare
• Data URL: https://figshare.com/collections/Alargeelectroencephalographicmotorimagerydatasetforelectroencephalographicbraincomputerinterfaces/3917698
• Publication year: 2018
• Keywords: EEG, motor imagery, brain-computer interface, BCI

References

M. Kaya, M. K. Binli, E. Ozbay, H. Yanar, and Y. Mishchenko, "A large electroencephalographic motor imagery dataset for electroencephalographic brain computer interfaces," Scientific Data, vol. 5, p. 180211, 2018. DOI: 10.1038/sdata.2018.211 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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