NM000139: eeg dataset, 9 subjects

BNCI 2014-001 Motor Imagery dataset

Access recordings and metadata through EEGDash.

Citation: 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 (2019). BNCI 2014-001 Motor Imagery dataset.

Modality: eeg Subjects: 9 Recordings: 108 License: CC-BY-ND-4.0 Source: nemar

Metadata: Complete (90%)

Quickstart

Get Started

Install

pip install eegdash

Access the data

from eegdash.dataset import NM000139

dataset = NM000139(cache_dir="./data")
# Get the raw object of the first recording
raw = dataset.datasets[0].raw
print(raw.info)

Query & Filter

Filter by subject

dataset = NM000139(cache_dir="./data", subject="01")

Advanced query

dataset = NM000139(
    cache_dir="./data",
    query={"subject": {"$in": ["01", "02"]}},
)

Iterate recordings

for rec in dataset:
    print(rec.subject, rec.raw.info['sfreq'])

Cite This Dataset

If you use this dataset in your research, please cite the original authors.

BibTeX

@dataset{nm000139,
  title = {BNCI 2014-001 Motor Imagery dataset},
  author = {Michael Tangermann and Klaus-Robert Müller and Ad Aertsen and Niels Birbaumer and Christoph Braun and Clemens Brunner and Robert Leeb and Carsten Mehring and Kai J. Miller and Gernot R. Müller-Putz and Guido Nolte and Gert Pfurtscheller and Hubert Preissl and Gerwin Schalk and Alois Schlögl and Carmen Vidaurre and Stephan Waldert and Benjamin Blankertz},
}

About This Dataset

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

View full README

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: left_hand=1, right_hand=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: left_hand, right_hand, 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: left_hand, right_hand, 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: {‘low_cutoff_hz’: 0.05, ‘high_cutoff_hz’: 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 BNCI2014_004 : BCI Competition 2008 2-class motor imagery (Dataset B) BNCI2003_004 : BCI Competition III 2-class motor imagery Examples

>> from moabb.datasets import BNCI2014_001 >>> dataset = BNCI2014_001() >>> 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

Dataset Information

Dataset ID	NM000139
Title	BNCI 2014-001 Motor Imagery dataset
Author (year)	Tangermann2014
Canonical	BNCI2014001, BCICIV1, BCICompIV1
Importable as	NM000139, Tangermann2014, BNCI2014001, BCICIV1, BCICompIV1
Year	2019
Authors	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
License	CC-BY-ND-4.0
Citation / DOI	Unknown
Source links	OpenNeuro | NeMAR | Source URL

Found an issue with this dataset?

If you encounter any problems with this dataset (missing files, incorrect metadata, loading errors, etc.), please let us know!

Report an Issue on GitHub

Technical Details

Subjects & recordings

• Subjects: 9

• Recordings: 108

• Tasks: 1

Channels & sampling rate

• Channels: 22

• Sampling rate (Hz): 250.0

• Duration (hours): 11.60808

Tags

• Pathology: Healthy

• Modality: Multisensory

• Type: Motor

Files & format

• Size on disk: 672.8 MB

• File count: 108

• Format: BIDS

License & citation

• License: CC-BY-ND-4.0

• DOI: —

Provenance

• Source: nemar

• OpenNeuro: nm000139

• NeMAR: nm000139

API Reference

Use the NM000139 class to access this dataset programmatically.

class eegdash.dataset.NM000139(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)

Bases: EEGDashDataset

BNCI 2014-001 Motor Imagery dataset

Study:

nm000139 (NeMAR)

Author (year):

Tangermann2014

Canonical:

BNCI2014001, BCICIV1, BCICompIV1

Also importable as: NM000139, Tangermann2014, BNCI2014001, BCICIV1, BCICompIV1.

Modality: eeg; Experiment type: Motor; Subject type: Healthy. Subjects: 9; recordings: 108; tasks: 1.

Parameters:

• cache_dir (str | Path) – Directory where data are cached locally.

• query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.

• s3_bucket (str | None) – Base S3 bucket used to locate the data.

• **kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:

Path

query

Merged query with the dataset filter applied.

Type:

dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:

list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/nm000139 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=nm000139

Examples

>>> from eegdash.dataset import NM000139
>>> dataset = NM000139(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

__init__(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)

save(path, overwrite=False)

Save the dataset to disk.

Parameters:

• path (str or Path) – Destination file path.

• overwrite (bool, default False) – If True, overwrite existing file.

Return type:

None

See Also

• eegdash.dataset.EEGDashDataset

• eegdash.dataset

• OpenNeuro dataset page

• NeMAR dataset page

• eegdash.dataset.DS001785

• eegdash.dataset.DS001787

• eegdash.dataset.DS001810

• eegdash.dataset.DS001849

• eegdash.dataset.DS001971