Motor imagery dataset for three imaginary states of the same upper extremity

Motor imagery dataset for three imaginary states of the same upper extremity.

Dataset Overview

• Code: Tavakolan2017
• Paradigm: imagery
• DOI: 10.1371/journal.pone.0174161
• Subjects: 12
• Sessions per subject: 4
• Events: rest=1, righthand=2, rightelbow_flexion=3
• Trial interval: [0, 3] s
• File format: BCI2000

Acquisition

• Sampling rate: 1000.0 Hz
• Number of channels: 32
• Channel types: eeg=32
• Montage: GSN-HydroCel-32
• Hardware: EGI Geodesic Net Amps 400 series
• Reference: Cz
• Sensor type: Ag/AgCl sponge
• Line frequency: 60.0 Hz
• Online filters: {'bandpass': [0.1, 100]}
• Impedance threshold: 50 kOhm

Participants

• Number of subjects: 12
• Health status: healthy
• Species: human

Experimental Protocol

• Paradigm: imagery
• Number of classes: 3
• Class labels: rest, righthand, rightelbow_flexion
• Trial duration: 3.0 s
• Study design: Three-class motor imagery of the same upper extremity: rest, grasping (MI-GRASP), and elbow flexion (MI-ELBOW). 20 trials per class per session, 4 sessions per subject.
• Feedback type: none
• Stimulus type: visual cue
• Stimulus modalities: visual
• Primary modality: visual
• Synchronicity: synchronous
• Mode: offline
• Instructions: REST: relax without movement. MI-GRASP: imagine opening and closing all fingers to grab an object. MI-ELBOW: imagine moving the forearm up and down.

HED Event Annotations

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

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

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

  right_elbow_flexion
    ├─ Sensory-event, Experimental-stimulus, Visual-presentation
    └─ Agent-action
       └─ Imagine
          ├─ Flex
          └─ Right, Elbow

Paradigm-Specific Parameters

• Detected paradigm: motor_imagery
• Imagery tasks: rest, righthand, rightelbow_flexion
• Cue duration: 3.0 s
• Imagery duration: 3.0 s

Data Structure

• Trials: 2880
• Trials per class: rest=20, righthand=20, rightelbow_flexion=20
• Trials context: 12 subjects x 4 sessions x 60 trials (20 per class)

Preprocessing

• Data state: continuous

Signal Processing

• Classifiers: SVM-RBF
• Feature extraction: autoregressivecoefficients, waveformlength, rootmeansquare
• Frequency bands: bandpass=[6.0, 35.0] Hz

Cross-Validation

• Method: 10x10-fold
• Folds: 10
• Evaluation type: within_subject

BCI Application

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

Tags

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

Documentation

• DOI: 10.1371/journal.pone.0174161
• License: CC0-1.0
• Investigators: Mojgan Tavakolan, Zack Frehlick, Xinyi Yong, Carlo Menon
• Senior author: Carlo Menon
• Institution: Simon Fraser University
• Department: MENRVA Research Group, Schools of Mechatronic Systems Engineering and Engineering Science
• Country: CA
• Repository: Zenodo
• Data URL: https://zenodo.org/records/18967205
• Publication year: 2017
• Ethics approval: Simon Fraser University Office of Research Ethics
• Keywords: motor imagery, EEG, upper extremity, same limb, time-domain features, SVM, BCI

References

M. Tavakolan, Z. Frehlick, X. Yong, and C. Menon, "Classifying three imaginary states of the same upper extremity using time-domain features," PLoS ONE, vol. 12, no. 3, e0174161, 2017. DOI: 10.1371/journal.pone.0174161

M. Tavakolan, Z. Frehlick, X. Yong, and C. Menon, "Data from: Classifying three imaginary states of the same upper extremity using time-domain features," Dryad, 2017. DOI: 10.5061/dryad.6qs86 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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