BNCI 2015-004 Mental tasks dataset

BNCI 2015-004 Mental tasks dataset.

Dataset Overview

• Code: BNCI2015-004
• Paradigm: imagery
• DOI: 10.1371/journal.pone.0123727
• Subjects: 9
• Sessions per subject: 2
• Events: math=1, letter=2, rotation=3, count=4, baseline=5
• Trial interval: [0, 4] s
• File format: gdf
• Data preprocessed: True

Acquisition

• Sampling rate: 256.0 Hz
• Number of channels: 30
• Channel types: eeg=30
• Channel names: AFz, F7, F3, Fz, F4, F8, FC3, FCz, FC4, T3, C3, Cz, C4, T4, CP3, CPz, CP4, P7, P5, P3, P1, Pz, P2, P4, P6, P8, PO3, PO4, O1, O2
• Montage: 10-20
• Hardware: g.tec
• Reference: left and right mastoid
• Ground: left and right mastoid
• Sensor type: active electrode
• Line frequency: 50.0 Hz
• Online filters: 0.5-100 Hz bandpass, 50 Hz notch
• Cap manufacturer: g.tec
• Electrode type: g.LADYbird active electrodes
• Auxiliary channels: EOG (2 ch, horizontal, vertical)

Participants

• Number of subjects: 9
• Health status: CNS tissue damage
• Clinical population: stroke and spinal cord injury
• Age: mean=38.0, std=10.0, min=20, max=57
• Gender distribution: male=2, female=7
• Handedness: not specified
• BCI experience: naive
• Species: human

Experimental Protocol

• Paradigm: imagery
• Number of classes: 5
• Class labels: math, letter, rotation, count, baseline
• Trial duration: 11.0 s
• Tasks: wordassociation, mentalsubtraction, spatialnavigation, righthandimagery, feetimagery
• Study design: Five mental tasks: word association (WORD), mental subtraction (SUB), spatial navigation (NAV), motor imagery of right hand (HAND), and motor imagery of both feet (FEET). Cue-guided paradigm with 7 seconds of continuous mental imagery per trial.
• Feedback type: none
• Stimulus type: visual cue
• Stimulus modalities: visual
• Primary modality: visual
• Synchronicity: synchronous
• Mode: screening
• Instructions: Participants were asked to continuously perform the specified mental imagery task for 7 seconds. For MI: kinesthetic imagination of movement (e.g., squeezing a rubber ball for hand, dorsiflexion for feet). For WORD: generate words beginning with presented letter. For SUB: successive elementary subtractions. For NAV: spatial navigation.

HED Event Annotations

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

  math
    ├─ Sensory-event, Experimental-stimulus, Visual-presentation
    └─ Agent-action
       └─ Imagine
          ├─ Think
          └─ Label/math

  letter
    ├─ Sensory-event, Experimental-stimulus, Visual-presentation
    └─ Agent-action
       └─ Imagine
          ├─ Think
          └─ Label/letter

  rotation
    ├─ Sensory-event, Experimental-stimulus, Visual-presentation
    └─ Agent-action
       └─ Imagine
          ├─ Think
          └─ Label/rotation

  count
    ├─ Sensory-event, Experimental-stimulus, Visual-presentation
    └─ Agent-action
       └─ Imagine, Count

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

Paradigm-Specific Parameters

• Detected paradigm: motor_imagery
• Imagery tasks: righthand, feet, wordassociation, mentalsubtraction, spatialnavigation
• Cue duration: 1.0 s
• Imagery duration: 7.0 s

Data Structure

• Trials: 40
• Blocks per session: 8
• Trials context: perclassper_day

Preprocessing

• Data state: filtered
• Preprocessing applied: True
• Steps: bandpass filter, notch filter, artifact rejection
• Highpass filter: 0.5 Hz
• Lowpass filter: 100.0 Hz
• Bandpass filter: {'lowcutoffhz': 0.5, 'highcutoffhz': 100.0}
• Notch filter: [50] Hz
• Artifact methods: manual artifact rejection based on EOG
• Re-reference: left and right mastoid

Signal Processing

• Classifiers: LDA
• Feature extraction: bandpower, temporal features
• Frequency bands: mu=[8, 12] Hz; beta=[13, 30] Hz

Cross-Validation

• Method: 10-fold cross-validation
• Folds: 10
• Evaluation type: withinsession, crosssession

Performance (Original Study)

• Accuracy: 77.0%
• Best Task Pair Gmac: 77.0
• Sub Vs Feet Gmac: 77.0
• Word Vs Hand Gmac: 70.0
• Hand Vs Feet Gmac: 64.0
• Between Day Word Vs Hand Gmac: 82.0

BCI Application

• Applications: communication, motorfunctionrestoration
• Environment: rehabilitation center
• Online feedback: False

Tags

• Pathology: Stroke, Spinal Cord Injury, CNS Damage
• Modality: Motor, Cognitive
• Type: Motor, Cognitive

Documentation

• DOI: 10.1371/journal.pone.0123727
• License: CC-BY-NC-ND-4.0
• Investigators: Reinhold Scherer, Josef Faller, Elisabeth V. C. Friedrich, Eloy Opisso, Ursula Costa, Andrea Kübler, Gernot R. Müller-Putz
• Senior author: Reinhold Scherer
• Contact: reinhold.scherer@tugraz.at
• Institution: Institut Guttmann
• Department: Institut Universitari de Neurorehabilitació adscrit a la UAB
• Address: 08916 Badalona, Barcelona, Spain
• Country: Spain
• Repository: BNCI Horizon 2020
• Data URL: https://bnci-horizon-2020.eu/database/data-sets
• Publication year: 2015
• Funding: FP7 EU Research Projects BrainAble (No. 247447); ABC (No. 287774); BackHome (No. 288566)
• Ethics approval: Comitè d'Ètica Assistencial de l'Institut Guttman
• Keywords: brain-computer interface, motor imagery, mental tasks, EEG, CNS tissue damage, stroke, spinal cord injury, binary classification

References

Zhang, X., Yao, L., Zhang, Q., Kanhere, S., Sheng, M., & Liu, Y. (2017). A survey on deep learning based brain computer interface: Recent advances and new frontiers. IEEE Transactions on Cognitive and Developmental Systems, 10(2), 145-163.

Notes

.. note::

`BNCI2015_004 was previously named BNCI2015004. BNCI2015004` will be removed in version 1.1.

.. versionadded:: 0.4.0 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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