BNCI 2015-001 Motor Imagery dataset

BNCI 2015-001 Motor Imagery dataset.

Dataset Overview

• Code: BNCI2015-001
• Paradigm: imagery
• DOI: 10.1109/tnsre.2012.2189584
• Subjects: 12
• Sessions per subject: 2
• Events: right_hand=1, feet=2
• Trial interval: [0, 5] s
• File format: gdf
• Data preprocessed: True

Acquisition

• Sampling rate: 512.0 Hz
• Number of channels: 13
• Channel types: eeg=13
• Channel names: FC3, FCz, FC4, C5, C3, C1, Cz, C2, C4, C6, CP3, CPz, CP4
• Montage: 10-20
• Hardware: g.tec
• Software: Matlab
• Reference: Car
• Sensor type: active electrode
• Line frequency: 50.0 Hz
• Online filters: 50 Hz notch
• Cap manufacturer: g.tec
• Cap model: g.GAMMAsys
• Auxiliary channels: gsr

Participants

• Number of subjects: 12
• Health status: healthy
• Age: mean=24.8
• Gender distribution: male=7, female=5
• Handedness: all right-handed
• BCI experience: naive
• Species: human

Experimental Protocol

• Paradigm: imagery
• Number of classes: 2
• Class labels: right_hand, feet
• Trial duration: 11.0 s
• Study design: Two-class motor imagery: sustained right hand movement imagery (palmar grip) versus both feet movement imagery (plantar extension)
• Feedback type: visual
• Stimulus type: cursor_feedback
• Stimulus modalities: visual, auditory
• Primary modality: visual
• Synchronicity: synchronous
• Mode: training
• Instructions: Relax during reference period (3s), perform sustained kinesthetic movement imagery during activity period. Condition 1 (arrow right): imagine palmar grip with right hand. Condition 2 (arrow down): imagine plantar extension of both feet.

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

  feet
    ├─ Sensory-event, Experimental-stimulus, Visual-presentation
    └─ Agent-action
       └─ Imagine, Move, Foot

Paradigm-Specific Parameters

• Detected paradigm: motor_imagery
• Imagery tasks: righthandpalmargrip, bothfeetplantarextension
• Cue duration: 1.25 s
• Imagery duration: 4.0 s

Data Structure

• Trials: 200
• Trials per class: right_hand=100, feet=100
• Trials context: per_session

Preprocessing

• Data state: filtered
• Preprocessing applied: True
• Steps: bandpass filter, notch filter
• Highpass filter: 0.5 Hz
• Lowpass filter: 100.0 Hz
• Bandpass filter: {'lowcutoffhz': 0.5, 'highcutoffhz': 100.0}
• Notch filter: [50.0] Hz
• Re-reference: car

Signal Processing

• Classifiers: LDA
• Feature extraction: logarithmic bandpower, CSP
• Frequency bands: alpha=[10, 13] Hz; beta=[16, 24] Hz

Cross-Validation

• Method: leave-one-out
• Evaluation type: cross_session

Performance (Original Study)

• Accuracy: 80.0%

BCI Application

• Applications: communication, control
• Online feedback: True

Tags

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

Documentation

• DOI: 10.1109/tnsre.2012.2189584
• License: CC-BY-NC-ND-4.0
• Investigators: Josef Faller, Carmen Vidaurre, Teodoro Solis-Escalante, Christa Neuper, Reinhold Scherer
• Senior author: Reinhold Scherer
• Contact: josef.faller@tugraz.at; christa.neuper@uni-graz.at; carmen.vidaurre@tu-berlin.de
• Institution: Graz University of Technology
• Department: Institute of Knowledge Discovery
• Address: 8010 Graz, Austria
• Country: Austria
• Repository: BNCI Horizon
• Publication year: 2012
• Funding: FP7 Framework EU Research Project BrainAble (No. 247447)

References

Faller, J., Vidaurre, C., Solis-Escalante, T., Neuper, C., & Scherer, R. (2012). Autocalibration and recurrent adaptation: Towards a plug and play online ERD-BCI. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 20(3), 313-319. https://doi.org/10.1109/tnsre.2012.2189584

Notes

.. note::

`BNCI2015_001 was previously named BNCI2015001. BNCI2015001` 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

---

Generated by MOABB 1.4.3 (Mother of All BCI Benchmarks) https://github.com/NeuroTechX/moabb