Munich Motor Imagery dataset

Munich Motor Imagery dataset.

Dataset Overview

• Code: GrosseWentrup2009
• Paradigm: imagery
• DOI: 10.1109/TBME.2008.2009768
• Subjects: 10
• Sessions per subject: 1
• Events: righthand=2, lefthand=1
• Trial interval: [0, 7] s
• File format: set
• Data preprocessed: True

Acquisition

• Sampling rate: 500.0 Hz
• Number of channels: 128
• Channel types: eeg=128
• Channel names: 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111, 112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125, 126, 127, 128
• Montage: standard_1020
• Hardware: BrainAmp
• Reference: Cz
• Line frequency: 50.0 Hz
• Online filters: {'highpasstimeconstant_s': 10}
• Impedance threshold: 10 kOhm

Participants

• Number of subjects: 10
• Health status: healthy
• Age: mean=25.6, std=2.5
• Gender distribution: male=8, female=2
• Handedness: {'right': 8}
• BCI experience: mixed
• Species: human

Experimental Protocol

• Paradigm: imagery
• Task type: motor_imagery
• Number of classes: 2
• Class labels: righthand, lefthand
• Trial duration: 10 s
• Tasks: motor_imagery
• Study design: two-class motor imagery with arrow cues
• Feedback type: none
• Stimulus type: arrow_cue
• Stimulus modalities: visual
• Primary modality: visual
• Synchronicity: synchronous
• Mode: offline
• Instructions: Subjects were instructed to perform haptic motor imagery of the left or the right hand during display of the arrow, as indicated by the direction of the arrow

HED Event Annotations

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

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

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

Paradigm-Specific Parameters

• Detected paradigm: motor_imagery
• Imagery tasks: lefthand, righthand
• Cue duration: 7.0 s
• Imagery duration: 7.0 s

Data Structure

• Trials: 150
• Trials context: per_class

Preprocessing

• Data state: preprocessed
• Preprocessing applied: True
• Artifact methods: none
• Re-reference: car
• Notes: No trials were rejected and no artifact correction was performed. Data were re-referenced to common average reference offline.

Signal Processing

• Classifiers: Logistic Regression
• Feature extraction: CSP, Beamforming, Laplacian, Bandpower
• Frequency bands: analyzed=[7.0, 30.0] Hz
• Spatial filters: CSP, Beamforming, Laplacian

Cross-Validation

• Method: bootstrapping
• Evaluation type: within_subject

BCI Application

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

Tags

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

Documentation

• DOI: 10.1109/TBME.2008.2009768
• License: CC-BY-4.0
• Investigators: Moritz Grosse-Wentrup, Christian Liefhold, Klaus Gramann, Martin Buss
• Senior author: Martin Buss
• Contact: moritzgw@ieee.org
• Institution: Technische Universität München
• Department: Institute of Automatic Control Engineering (LSR)
• Country: DE
• Repository: Zenodo
• Publication year: 2009
• Keywords: Beamforming, brain-computer interfaces, common spatial patterns, electroencephalography, motor imagery, spatial filtering

References

Grosse-Wentrup, Moritz, et al. "Beamforming in noninvasive brain–computer interfaces." IEEE Transactions on Biomedical Engineering 56.4 (2009): 1209-1219. 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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