Motor execution dataset from Wairagkar et al 2018

Motor execution dataset from Wairagkar et al 2018.

Dataset Overview

• Code: Wairagkar2018
• Paradigm: imagery
• DOI: 10.1371/journal.pone.0193722
• Subjects: 14
• Sessions per subject: 1
• Events: right_hand=1, rest=2, left_hand=3
• Trial interval: [0, 3] s
• File format: MAT
• Data preprocessed: True

Acquisition

• Sampling rate: 1024.0 Hz
• Number of channels: 19
• Channel types: eeg=19
• Channel names: Fp1, Fp2, F7, F3, Fz, F4, F8, T7, C3, Cz, C4, T8, P7, P3, Pz, P4, P8, O1, O2
• Montage: standard_1020
• Hardware: Deymed TruScan 32
• Reference: FCz
• Ground: AFz
• Sensor type: Ag/AgCl ring
• Line frequency: 50.0 Hz
• Online filters: {'highpass': 0.5, 'lowpass': 60, 'notch_hz': 50}

Participants

• Number of subjects: 14
• Health status: healthy
• Age: mean=26.0, std=4.0
• Gender distribution: female=8, male=6
• Handedness: mixed (12 right, 2 left)
• BCI experience: naive
• Species: human

Experimental Protocol

• Paradigm: imagery
• Number of classes: 3
• Class labels: right_hand, rest, left_hand
• Trial duration: 6.0 s
• Study design: Asynchronous voluntary finger tapping: right tap, left tap, and resting state
• Feedback type: none
• Stimulus type: text cues
• Stimulus modalities: visual
• Primary modality: visual
• Synchronicity: asynchronous
• Mode: offline
• Instructions: Participants were asked to tap their index finger at a self-chosen time within a 10-second window after the cue

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

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

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

Paradigm-Specific Parameters

• Detected paradigm: motor_imagery
• Imagery tasks: right_hand, left_hand, rest

Data Structure

• Trials: 1665
• Trials context: 14 subjects x 120 trials (40 per condition), except subject 2 with 105 trials (35 per condition)

Preprocessing

• Data state: preprocessed
• Preprocessing applied: True
• Steps: DC offset removal, 0.5 Hz high-pass filter, 50 Hz notch filter, 60 Hz low-pass filter, ICA artifact removal (EEGLAB infomax), trial segmentation (-3 to +3 s around movement onset)
• Highpass filter: 0.5 Hz
• Lowpass filter: 60.0 Hz
• Notch filter: 50.0 Hz

Signal Processing

• Classifiers: LDA
• Feature extraction: autocorrelation_relaxation_time, ERD
• Frequency bands: broadband=[0.5, 30.0] Hz; mu=[8.0, 13.0] Hz; beta=[13.0, 30.0] Hz; low=[0.5, 8.0] Hz
• Spatial filters: bipolar_montage

Cross-Validation

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

BCI Application

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

Tags

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

Documentation

• DOI: 10.1371/journal.pone.0193722
• License: CC-BY-4.0
• Investigators: Maitreyee Wairagkar, Yoshikatsu Hayashi, Slawomir J. Nasuto
• Senior author: Slawomir J. Nasuto
• Institution: University of Reading
• Department: Brain Embodiment Lab, Biomedical Engineering
• Country: GB
• Repository: University of Reading Research Data Archive
• Data URL: https://researchdata.reading.ac.uk/117/
• Publication year: 2018

References

Wairagkar, M., Hayashi, Y., & Nasuto, S. J. (2018). Exploration of neural correlates of movement intention based on characterisation of temporal dependencies in electroencephalography. PLOS ONE, 13(3), e0193722. https://doi.org/10.1371/journal.pone.0193722 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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