Motor imagery BCI dataset with pupillometry augmentation

Motor imagery BCI dataset with pupillometry augmentation.

Dataset Overview

• Code: Rozado2015
• Paradigm: imagery
• DOI: 10.1371/journal.pone.0121262
• Subjects: 30
• Sessions per subject: 1
• Events: left_hand=1, rest=2
• Trial interval: [0.0, 6.0] s
• Runs per session: 2
• File format: XDF

Acquisition

• Sampling rate: 512.0 Hz
• Number of channels: 32
• Channel types: eeg=32
• Montage: biosemi32
• Hardware: BioSemi ActiveTwo
• Reference: CMS/DRL
• Sensor type: active
• Line frequency: 50.0 Hz
• Cap manufacturer: BioSemi
• Electrode material: sintered Ag/AgCl

Participants

• Number of subjects: 30
• Health status: healthy
• Age: mean=38.0, std=9.69, min=15, max=61
• Gender distribution: male=15, female=15
• Handedness: {'right': 27, 'left': 3}

Experimental Protocol

• Paradigm: imagery
• Task type: left hand grasping imagery vs rest
• Number of classes: 2
• Class labels: left_hand, rest
• Trial duration: 6.0 s
• Study design: Motor imagery with pupillometry augmentation
• Feedback type: none
• Stimulus type: auditory cue
• Stimulus modalities: auditory
• Primary modality: auditory
• 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

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

Paradigm-Specific Parameters

• Detected paradigm: motor_imagery
• Imagery tasks: left hand grasping, rest
• Imagery duration: 6.0 s

Data Structure

• Blocks per session: 2
• Block duration: 300.0 s
• Trials context: 2 experiments of 25 trials each (50 trials total per subject). Each experiment is stored as one XDF file.

Signal Processing

• Classifiers: LDA
• Feature extraction: CSP, pupil_diameter
• Frequency bands: bandpass=[8.0, 30.0] Hz
• Spatial filters: CSP

Cross-Validation

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

BCI Application

• Environment: lab
• Online feedback: False

Tags

• Pathology: healthy
• Modality: auditory
• Type: motor_imagery

Documentation

• DOI: 10.1371/journal.pone.0121262
• License: CC0 1.0
• Investigators: David Rozado, Andreas Duenser, Ben Howell
• Senior author: David Rozado
• Institution: CSIRO
• Department: Digital Productivity Flagship
• Country: AU
• Repository: Harvard Dataverse
• Data URL: https://doi.org/10.7910/DVN/28932
• Publication year: 2015
• Keywords: motor imagery, BCI, pupillometry, EEG, brain-computer interface

References

D. Rozado, T. Duenser, and B. Gruen, "Improving the performance of an EEG-based motor imagery brain computer interface using task evoked changes in pupil diameter," PLoS ONE, vol. 10, no. 3, e0121262, 2015. DOI: 10.1371/journal.pone.0121262 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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