NM000143: eeg dataset, 5 subjects#

BNCI2003_IVa Motor Imagery dataset

Access recordings and metadata through EEGDash.

Citation: Guido Dornhege, Benjamin Blankertz, Gabriel Curio, Klaus-Robert Müller (2019). BNCI2003_IVa Motor Imagery dataset.

Modality: eeg Subjects: 5 Recordings: 5 License: CC-BY-4.0 Source: nemar

Metadata: Complete (90%)

Quickstart#

Install

pip install eegdash

Access the data

from eegdash.dataset import NM000143

dataset = NM000143(cache_dir="./data")
# Get the raw object of the first recording
raw = dataset.datasets[0].raw
print(raw.info)

Filter by subject

dataset = NM000143(cache_dir="./data", subject="01")

Advanced query

dataset = NM000143(
    cache_dir="./data",
    query={"subject": {"$in": ["01", "02"]}},
)

Iterate recordings

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

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

BibTeX

@dataset{nm000143,
  title = {BNCI2003_IVa Motor Imagery dataset},
  author = {Guido Dornhege and Benjamin Blankertz and Gabriel Curio and Klaus-Robert Müller},
}

About This Dataset#

BNCI2003_IVa Motor Imagery dataset

BNCI2003_IVa Motor Imagery dataset.

Dataset Overview

  • Code: BNCI2003-004

  • Paradigm: imagery

  • DOI: 10.1109/TBME.2004.827088

View full README

BNCI2003_IVa Motor Imagery dataset

BNCI2003_IVa Motor Imagery dataset.

Dataset Overview

  • Code: BNCI2003-004

  • Paradigm: imagery

  • DOI: 10.1109/TBME.2004.827088

  • Subjects: 5

  • Sessions per subject: 1

  • Events: right_hand=0, feet=1

  • Trial interval: [0, 3.5] s

  • File format: mat

  • Data preprocessed: True

Acquisition

  • Sampling rate: 100.0 Hz

  • Number of channels: 118

  • Channel types: eeg=118

  • Channel names: AF3, AF4, AF7, AF8, AFp1, AFp2, C1, C2, C3, C4, C5, C6, CCP1, CCP2, CCP3, CCP4, CCP5, CCP6, CCP7, CCP8, CFC1, CFC2, CFC3, CFC4, CFC5, CFC6, CFC7, CFC8, CP1, CP2, CP3, CP4, CP5, CP6, CPz, Cz, F1, F2, F3, F4, F5, F6, F7, F8, FAF1, FAF2, FAF5, FAF6, FC1, FC2, FC3, FC4, FC5, FC6, FCz, FFC1, FFC2, FFC3, FFC4, FFC5, FFC6, FFC7, FFC8, FT10, FT7, FT8, FT9, Fp1, Fp2, Fpz, Fz, I1, I2, O1, O2, OI1, OI2, OPO1, OPO2, Oz, P1, P10, P2, P3, P4, P5, P6, P7, P8, P9, PCP1, PCP2, PCP3, PCP4, PCP5, PCP6, PCP7, PCP8, PO1, PO2, PO3, PO4, PO7, PO8, POz, PPO1, PPO2, PPO5, PPO6, PPO7, PPO8, Pz, T7, T8, TP10, TP7, TP8, TP9

  • Montage: standard_1005

  • Hardware: BrainAmp

  • Sensor type: EEG

  • Line frequency: 50.0 Hz

  • Online filters: {‘bandpass’: [0.05, 200]}

Participants

  • Number of subjects: 5

  • Health status: healthy

Experimental Protocol

  • Paradigm: imagery

  • Number of classes: 2

  • Class labels: right_hand, feet

  • Trial duration: 3.5 s

  • Stimulus type: visual cue

  • Mode: offline

  • Instructions: subjects performed motor imagery (left hand, right hand, or right foot) according to visual cue for 3.5 seconds

  • Stimulus presentation: duration=3.5 s, interval=1.75-2.25 s random, modality=visual

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: right_hand, feet

  • Cue duration: 3.5 s

Data Structure

  • Trials: 280

  • Trials context: 280 cues per subject, split into labeled training and unlabeled test sets (varying per subject)

Preprocessing

  • Data state: downsampled to 100 Hz for offline analysis

  • Preprocessing applied: True

  • Steps: bandpass filtering, downsampling

  • Bandpass filter: {‘low_cutoff_hz’: 0.05, ‘high_cutoff_hz’: 200.0}

  • Downsampled to: 100 Hz

  • Notes: Band-pass filtered 0.05-200 Hz during acquisition at 1000 Hz with 16-bit (0.1 uV) accuracy, then downsampled to 100 Hz by picking each 10th sample. Original experiment also recorded EMG and EOG but these are not in the shared data files.

Signal Processing

  • Classifiers: LDA, regularized LDA

  • Feature extraction: CSP, SUB (MRP/slow potentials), AR

  • Frequency bands: alpha=[8, 13] Hz; beta=[15, 25] Hz; alpha_beta=[7, 30] Hz

  • Spatial filters: CSP, spatial Laplacian

Cross-Validation

  • Method: 10x10-fold cross validation

  • Folds: 10

  • Evaluation type: within-subject

BCI Application

  • Applications: motor_control

  • Environment: laboratory

  • Online feedback: False

Tags

  • Pathology: Healthy

  • Modality: Motor

  • Type: Research

Documentation

  • DOI: 10.1109/TBME.2004.827088

  • License: CC-BY-4.0

  • Investigators: Guido Dornhege, Benjamin Blankertz, Gabriel Curio, Klaus-Robert Müller

  • Senior author: Klaus-Robert Müller

  • Contact: benjamin.blankertz@tu-berlin.de

  • Institution: Fraunhofer FIRST (IDA); Charité University Medicine Berlin

  • Department: Fraunhofer FIRST (IDA); Department of Neurology, Campus Benjamin Franklin

  • Address: 12489 Berlin, Germany; 12203 Berlin, Germany

  • Country: DE

  • Repository: BBCI

  • Publication year: 2004

  • Funding: Bundesministerium für Bildung und Forschung (BMBF) under Grants FKZ 01IBB02A and FKZ 01IBB02B

  • Keywords: brain-computer interface, BCI, common spatial patterns, electroencephalogram, EEG, event-related desynchronization, feature combination, movement related potential, multiclass, single-trial analysis

References

Guido Dornhege, Benjamin Blankertz, Gabriel Curio, and Klaus-Robert Muller. Boosting bit rates in non-invasive EEG single-trial classifications by feature combination and multi-class paradigms. IEEE Trans. Biomed. Eng., 51(6):993-1002, June 2004. Notes .. versionadded:: 0.4.0 This is one of the earliest and most influential motor imagery BCI datasets, used extensively for benchmarking classification algorithms. The dataset was part of BCI Competition III and has been cited in hundreds of papers. See Also BNCI2014_001 : BCI Competition IV 4-class motor imagery dataset BNCI2014_004 : BCI Competition 2008 2-class motor imagery dataset 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 1.5.0 (Mother of All BCI Benchmarks) https://github.com/NeuroTechX/moabb

Dataset Information#

Dataset ID

NM000143

Title

BNCI2003_IVa Motor Imagery dataset

Author (year)

BNCI2003

Canonical

BCICIII_IVa, BCICompIII_IVa, BNCI2003_IVa

Importable as

NM000143, BNCI2003, BCICIII_IVa, BCICompIII_IVa, BNCI2003_IVa

Year

2019

Authors

Guido Dornhege, Benjamin Blankertz, Gabriel Curio, Klaus-Robert Müller

License

CC-BY-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: 5

  • Recordings: 5

  • Tasks: 1

Channels & sampling rate

  • Channels: 118

  • Sampling rate (Hz): 100.0

  • Duration (hours): 3.9763027777777777

Tags

  • Pathology: Healthy

  • Modality: Visual

  • Type: Motor

Files & format

  • Size on disk: 492.7 MB

  • File count: 5

  • Format: BIDS

License & citation

  • License: CC-BY-4.0

  • DOI: —

Provenance

API Reference#

Use the NM000143 class to access this dataset programmatically.

class eegdash.dataset.NM000143(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#

Bases: EEGDashDataset

BNCI2003_IVa Motor Imagery dataset

Study:

nm000143 (NeMAR)

Author (year):

BNCI2003

Canonical:

BCICIII_IVa, BCICompIII_IVa, BNCI2003_IVa

Also importable as: NM000143, BNCI2003, BCICIII_IVa, BCICompIII_IVa, BNCI2003_IVa.

Modality: eeg; Experiment type: Motor; Subject type: Healthy. Subjects: 5; recordings: 5; 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/nm000143 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=nm000143

Examples

>>> from eegdash.dataset import NM000143
>>> dataset = NM000143(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()
__init__(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#
save(path, overwrite=False)[source]#

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#