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nm000139 NEMAR-native dataset

BNCI 2014-001 Motor Imagery dataset

The BNCI 2014-001 Motor Imagery dataset is a widely-used benchmark for brain-computer interface research, comprising EEG recordings from 9 healthy subjects performing four-class motor imagery tasks (left hand, right hand, feet, and tongue). Each subject completed two sessions with 6 runs per session, yielding 200 training and 240 test trials. The dataset features 22 EEG channels with two versions: original at 1000 Hz and downsampled to 100 Hz (using Chebyshev Type II filtering). Minimal preprocessing includes bandpass filtering (0.05-200 Hz) and 50 Hz notch filtering, making it a standard resource for evaluating multi-class motor imagery classification algorithms and cross-session transfer learning approaches.

EEG
Download zip Issues GitHub

Compute on this dataset

Two routes today, with a third (in-browser one-click submission) landing soon.

  1. NeuroScience Gateway (NSG) portal.

    NSG runs EEGLAB / Brainstorm / MNE pipelines on supercomputing time donated by SDSC. Create an account, point a job at this dataset's S3 prefix (s3://nemar/nm000139), and submit.
    nsgportal.org →

  2. Local processing with nemar-cli.

    Pull the dataset to your machine and run any toolbox locally. Honors the published version pinning. 

    npm install -g nemar-cli
nemar dataset clone nm000139
cd nm000139 && nemar dataset get
  3. Just the files.

    rclone, aria2c, or any HTTPS client works against data.nemar.org/nm000139/ — the manifest carries presigned S3 URLs.

Direct compute access is coming soon. One-click NSG submission from this page is scoped for a follow-up phase. Tracked on nemarOrg/website#6.
DOI 10.82901/nemar.nm000139

![DOI](https://doi.org/10.82901/nemar.nm000139)

BNCI 2014-001 Motor Imagery dataset

BNCI 2014-001 Motor Imagery dataset.

Dataset Overview

  • Code: BNCI2014-001
  • Paradigm: imagery
  • DOI: 10.3389/fnins.2012.00055
  • Subjects: 9
  • Sessions per subject: 2
  • Events: lefthand=1, righthand=2, feet=3, tongue=4
  • Trial interval: [2, 6] s
  • Runs per session: 6
  • File format: GDF
  • Data preprocessed: True

Acquisition

  • Sampling rate: 250.0 Hz
  • Number of channels: 25
  • Channel types: eeg=22, eog=3
  • Channel names: C1, C2, C3, C4, C5, C6, CP1, CP2, CP3, CP4, CPz, Cz, EOG1, EOG2, EOG3, FC1, FC2, FC3, FC4, FCz, Fz, P1, P2, POz, Pz
  • Montage: custom
  • Hardware: BrainAmp MR plus
  • Software: BCI2000
  • Reference: left mastoid
  • Ground: unknown
  • Sensor type: Ag/AgCl
  • Line frequency: 50.0 Hz
  • Online filters: bandpass 0.05-200 Hz
  • Cap manufacturer: EASYCAP GmbH

Participants

  • Number of subjects: 9
  • Health status: healthy
  • Species: human

Experimental Protocol

  • Paradigm: imagery
  • Number of classes: 4
  • Class labels: lefthand, righthand, feet, tongue
  • Trial duration: 4.0 s
  • Study design: Two-class motor imagery (selected from left hand, right hand, and foot) with asynchronous/continuous control periods
  • Feedback type: none
  • Stimulus type: arrow_cue
  • Stimulus modalities: visual, auditory
  • Primary modality: multisensory
  • Synchronicity: asynchronous
  • Mode: offline
  • Instructions: Subjects instructed to perform motor imagery during cued periods

HED Event Annotations

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

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

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

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

  tongue
    ├─ Sensory-event
    │  ├─ Experimental-stimulus
    │  ├─ Visual-presentation
    │  └─ Upward, Arrow
    └─ Agent-action
       └─ Imagine, Move, Tongue

Paradigm-Specific Parameters

  • Detected paradigm: motor_imagery
  • Imagery tasks: lefthand, righthand, foot
  • Cue duration: 4.0 s
  • Imagery duration: 4.0 s

Data Structure

  • Trials: {'training': 200, 'test': 240}
  • Blocks per session: 6
  • Trials context: per subject (2 training runs + 4 test runs)

Preprocessing

  • Data state: minimally preprocessed (bandpass and notch filtered)
  • Preprocessing applied: True
  • Steps: bandpass filtering
  • Highpass filter: 0.05 Hz
  • Lowpass filter: 200 Hz
  • Bandpass filter: {'lowcutoffhz': 0.05, 'highcutoffhz': 200.0}
  • Filter type: analog
  • Re-reference: none
  • Downsampled to: 100.0 Hz
  • Notes: Data provided in two versions: original at 1000 Hz and downsampled to 100 Hz (with Chebyshev Type II filter order 10, stop band ripple 50 dB, stop band edge 49 Hz)

Signal Processing

  • Classifiers: LDA, SVM, Neural Network, Naive Bayes, RBF Neural Network
  • Feature extraction: CSP, FBCSP, Bandpower, ERD, ERS
  • Frequency bands: mu=[8, 12] Hz; beta=[16, 24] Hz

Cross-Validation

  • Method: train-test split
  • Evaluation type: within_session

Performance (Original Study)

  • Mse: 0.382

BCI Application

  • Applications: cursor_control, communication
  • Environment: laboratory
  • Online feedback: False

Tags

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

Documentation

  • Description: Review of the BCI competition IV - Data set 1: Asynchronous Motor Imagery
  • DOI: 10.3389/fnins.2012.00055
  • License: CC-BY-ND-4.0
  • Investigators: Michael Tangermann, Klaus-Robert Müller, Ad Aertsen, Niels Birbaumer, Christoph Braun, Clemens Brunner, Robert Leeb, Carsten Mehring, Kai J. Miller, Gernot R. Müller-Putz, Guido Nolte, Gert Pfurtscheller, Hubert Preissl, Gerwin Schalk, Alois Schlögl, Carmen Vidaurre, Stephan Waldert, Benjamin Blankertz
  • Senior author: Michael Tangermann
  • Contact: michael.tangermann@tu-berlin.de
  • Institution: Berlin Institute of Technology
  • Department: Machine Learning Laboratory
  • Address: FR 6-9, Franklinstr. 28/29, 10587 Berlin, Germany
  • Country: AT
  • Repository: BNCI Horizon
  • Data URL: http://www.bbci.de/competition/iv/
  • Publication year: 2012
  • Keywords: brain-computer interface, BCI, competition

References

Tangermann, M., Muller, K.R., Aertsen, A., Birbaumer, N., Braun, C., Brunner, C., Leeb, R., Mehring, C., Miller, K.J., Mueller-Putz, G. and Nolte, G., 2012. Review of the BCI competition IV. Frontiers in neuroscience, 6, p.55.

Notes

.. note::

`BNCI2014_001 was previously named BNCI2014001. BNCI2014001` will be removed in version 1.1.

.. versionadded:: 0.4.0

This is one of the most widely used motor imagery datasets in BCI research, commonly referred to as "BCI Competition IV Dataset 2a". It serves as a standard benchmark for 4-class motor imagery classification algorithms.

The dataset is particularly useful for:

  • Multi-class motor imagery classification (4 classes) - Transfer learning studies (9 subjects, 2 sessions each) - Cross-session variability analysis

See Also

BNCI2014004 : BCI Competition 2008 2-class motor imagery (Dataset B) BNCI2003004 : BCI Competition III 2-class motor imagery

Examples

>>> from moabb.datasets import BNCI2014001 >>> dataset = BNCI2014001() >>> dataset.subject_list [1, 2, 3, 4, 5, 6, 7, 8, 9] 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

Files

15 top-level entries · 672 MB total