EEGdash›NeMAR›NM000143

Iss. 143 · 5 subjects · 5 recordings · CC-BY-4.0

Dataset Brief · BNCI2003_IVa Motor Imagery dataset

NM000143: eeg dataset, 5 subjects#

BNCI2003_IVa Motor Imagery dataset

Citation: Guido Dornhege, Benjamin Blankertz, Gabriel Curio, Klaus-Robert Müller (2019). BNCI2003_IVa Motor Imagery dataset. 10.82901/nemar.nm000143

5-participant EEG dataset — BNCI2003_IVa Motor Imagery dataset.

Data & curation Guido Dornhege · Benjamin Blankertz · Gabriel Curio · Klaus-Robert Müller
Year 2019 · Distributed via NeMAR
Funding Bundesministerium für Bildung und Forschung (BMBF) under Grants FKZ 01IBB02A and FKZ 01IBB02B

EEG · 118 ch100 HzBIDS 1.9.0Task · imageryHealthyVisualMotor

Layer 01Study

What was asked

Hypothesis, independent & dependent variables, paradigm, cohort, and the editorial caveats around what the recordings can and cannot answer.

Layer 02Signal · BIDS

What was recorded

Sidecars, channels & electrodes, coordinate system, event semantics, and quality stats from the NEMAR pipeline when available.

Layer 03Training · ML

What you can train on

Recommended access modes — MNE Raw, braindecode windows, PyTorch DataLoader — plus the targets the metadata makes addressable.

§ 01Access · Get started

Quickstart#

Get Started

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)

Query & Filter

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'])

Cite This Dataset

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},
  doi = {10.82901/nemar.nm000143},
  url = {https://doi.org/10.82901/nemar.nm000143},
}

§ 02Study · The README

About This Dataset#

BNCI2003_IVa Motor Imagery dataset.

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

DOI

BNCI2003_IVa Motor Imagery dataset

right_hand

View full README

DOI

BNCI2003_IVa Motor Imagery dataset

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) NeuroTechX/moabb

§ 03Cohort · Participants

Cohort#

Dataset Statistics#

Channel counts: 118 ch (n=5 recordings)

Sampling frequencies: 100.0 Hz (n=5 recordings)

Total recording duration: 3 h 58 min

§ 04Signal · Electrodes & trace

Signal · Electrodes & live trace#

Fig. 01 Signal & montage 118 ch · EEG · 100 Hz · 5 subjects, 5 recordings

Live trace viewer — sub-1 · ses-0train · task-imagery · run-0

Showing one representative recording out of 5 subjects and 5 recordings in this dataset. Browse the full set on OpenNeuro; drop any other _eeg.{set,edf,bdf,vhdr} file onto the viewer (or pass ?eeg=<url>) to inspect it.

Electrode layout — EEG · 92 sensors — 92 channels

NEMAR Processing Statistics#

The plots below are generated by NEMAR’s automated EEG pipeline. The histogram shows pipeline success for data cleaning and ICA decomposition, the percentage of data frames and EEG channels retained after artefact removal, line noise per channel (RMS, dB), and the age/gender distribution of participants.

HED event descriptors word cloud

§ 05Manifest · BIDS tree

Manifest#

File Explorer#

Browse the BIDS file structure of this dataset. Records are fetched on demand from the EEGDash catalog the first time you open the explorer.

Recordings—

Files—

Subjects—

Modalities—

Click to load file structure…

§ 06API · Programmatic access

API Reference#

Signature

eegdash.dataset

class

eegdash.dataset.NM000143(cache_dir, query=None, s3_bucket=None, **kwargs)

Bases: EEGDashDataset

Author (year)BNCI2003

Canonical—

Importable asNM000143 · BNCI2003

Sourceeegdash/dataset/registry.py · [source ↗]

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

BNCI2003_IVa Motor Imagery dataset

Study:: nm000143 (NeMAR)
Author (year):: BNCI2003
Canonical:: —

Also importable as: NM000143, BNCI2003.

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 DOI: https://doi.org/10.82901/nemar.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: str, overwrite: bool = False, offset: int = 0)[source]#

Save datasets to files by creating one subdirectory for each dataset:

path/
    0/
        0-raw.fif | 0-epo.fif
        description.json
        raw_preproc_kwargs.json (if raws were preprocessed)
        window_kwargs.json (if this is a windowed dataset)
        window_preproc_kwargs.json  (if windows were preprocessed)
        target_name.json (if target_name is not None and dataset is raw)
    1/
        1-raw.fif | 1-epo.fif
        description.json
        raw_preproc_kwargs.json (if raws were preprocessed)
        window_kwargs.json (if this is a windowed dataset)
        window_preproc_kwargs.json  (if windows were preprocessed)
        target_name.json (if target_name is not None and dataset is raw)

Parameters:

path (str) –

Directory in which subdirectories are created to store
-raw.fif | -epo.fif and .json files to.
overwrite (bool) – Whether to delete old subdirectories that will be saved to in this call.
offset (int) – If provided, the integer is added to the id of the dataset in the concat. This is useful in the setting of very large datasets, where one dataset has to be processed and saved at a time to account for its original position.

Access modesMNE → braindecode → PyTorch → ML

.rawMNE Raw object — standard tools (filter, epoch, ICA, plot_psd).mne

BaseConcatDatasetEach record is a lazy BaseDataset from braindecode — windowed via create_windows_from_events.braindecode

DataLoaderWraps the windowed dataset into a PyTorch DataLoader; supports parallel workers and on-the-fly augmentations.pytorch

Zarr cacheOptional braindecode Zarr mirror for fast resume; persisted to cache_dir.zarr

Hugging FaceNo per-dataset mirror published yet — browse the EEGDash org listing for sibling datasets. See the datasets loader API.huggingface

Croissant 1.0Machine-readable JSON-LD descriptor — NM000143.croissant.json (MLCommons schema, ingestible by PyTorch / TensorFlow / JAX).mlcommons

Examples using EEGDashcurated · start here

Find datasets with the EEGDash APIQuery the catalogue, filter by task or modality, list candidates.

Load one EEG recordingResolve a single record to an MNE Raw with channels and events.

EEG recording to PyTorch DataLoaderWrap braindecode windows in a DataLoader for model training.

Preprocess EEG and create windowsFilter, resample, epoch — and persist the windowed dataset.

Save and reload prepared dataCache a windowed dataset to disk and reattach it without recompute.

Download a dataset locallyPrefetch BIDS files to a local cache and validate the layout.

Swap any load_dataset(...) call for nm000143 to reproduce the tutorial on this dataset.

Citation

Guido Dornhege, Benjamin Blankertz, Gabriel Curio, Klaus-Robert Müller (2019). BNCI2003_IVa Motor Imagery dataset. 10.82901/nemar.nm000143

Provenance

¹Contributed to nemar in BIDS format.

²Curated & ingested by the EEGDash catalog; see CITATION.cff for canonical reference.

³Persistent identifier: 10.82901/nemar.nm000143.

Related & sibling datasets

NM000146EEG · 10 subj NM000137EEG · 7 subj NM000243EEG · 15 subj NM000135EEG · 1 subj NM000145EEG · 10 subj

+ 1 more — see See Also below →

BIDS

BIDS 1.9.0

Sidecars

events · events.json · channels · eeg.json

Provenance

CC-BY-4.0 · 10.82901/nemar.nm000143

Machine-readable

schema.org/Dataset · Croissant

Mirrors

OpenNeuro · NEMAR · HF org

Dataset ID	`NM000143`
Title	BNCI2003_IVa Motor Imagery dataset
Author (year)	`BNCI2003`
Canonical	—
Importable as	`NM000143`, `BNCI2003`
Year	2019
Authors	Guido Dornhege, Benjamin Blankertz, Gabriel Curio, Klaus-Robert Müller
License	CC-BY-4.0
Citation / DOI	10.82901/nemar.nm000143
Source links	OpenNeuro \| NeMAR \| Source URL

NM000143: eeg dataset, 5 subjects#

Quickstart#

About This Dataset#

Cohort#

Dataset Statistics#

Signal · Electrodes & live trace#

NEMAR Processing Statistics#

Manifest#

File Explorer#

API Reference#

Citation

Provenance

Related & sibling datasets

See Also#