EEGdash›NeMAR›NM000135

Iss. 135 · 1 subjects · 5 recordings · CC-BY-ND-4.0

Dataset Brief · BNCI 2014-004 Motor Imagery dataset

NM000135: eeg dataset, 1 subjects#

Name: BNCI 2014-004 Motor Imagery dataset
Published: 2019-01-01
License: CC-BY-ND-4.0

BNCI 2014-004 Motor Imagery dataset

Citation: R. Leeb, C. Brunner, G. R. Müller-Putz, A. Schlögl, G. Pfurtscheller, F. Lee, C. Keinrath, R. Scherer, H. Bischof (2019). BNCI 2014-004 Motor Imagery dataset. 10.82901/nemar.nm000135

1-participant EEG dataset — BNCI 2014-004 Motor Imagery dataset.

Data & curation R. Leeb · C. Brunner · G. R. Müller-Putz · A. Schlögl · G. Pfurtscheller · F. Lee · …
Year 2019 · Distributed via NeMAR

EEG · 3 ch250 HzBIDS 1.9.0Task · imagery5 sessionsHealthyVisualMotor

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 NM000135

dataset = NM000135(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 = NM000135(cache_dir="./data", subject="01")

Advanced query

dataset = NM000135(
    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{nm000135,
  title = {BNCI 2014-004 Motor Imagery dataset},
  author = {R. Leeb and C. Brunner and G. R. Müller-Putz and A. Schlögl and G. Pfurtscheller and F. Lee and C. Keinrath and R. Scherer and H. Bischof},
  doi = {10.82901/nemar.nm000135},
  url = {https://doi.org/10.82901/nemar.nm000135},
}

§ 02Study · The README

About This Dataset#

BNCI 2014-004 Motor Imagery dataset.

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

DOI

BNCI 2014-004 Motor Imagery dataset

left_hand

View full README

DOI

BNCI 2014-004 Motor Imagery dataset

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

Paradigm-Specific Parameters

Detected paradigm: motor_imagery
Imagery tasks: left_hand, right_hand
Cue duration: 1.25 s
Imagery duration: 4.0 s

Data Structure

Trials: {‘screening’: 120, ‘feedback’: 160}
Trials context: per session

Preprocessing

Data state: raw with online filtering
Preprocessing applied: True
Steps: bandpass filtering, notch filtering
Highpass filter: 0.5 Hz
Lowpass filter: 100.0 Hz
Bandpass filter: {‘low_cutoff_hz’: 0.5, ‘high_cutoff_hz’: 100.0}
Notch filter: [50.0] Hz
Filter type: analog
Notes: Online bandpass (0.5-100 Hz) and notch (50 Hz) filters applied during recording. Artifact trials marked with event type 1023. EOG channels provided for user-applied artifact correction.

Signal Processing

Classifiers: LDA
Feature extraction: Bandpower, BP

Cross-Validation

Method: 10x10 cross-validation
Folds: 10
Evaluation type: within_subject

BCI Application

Applications: motor_control
Environment: laboratory
Online feedback: True

Tags

Pathology: Healthy
Modality: Motor
Type: Motor Imagery

Documentation

Description: BCI Competition 2008 - Graz data set B: Two-class motor imagery dataset (left/right hand) with screening sessions (no feedback) and smiley feedback sessions. 9 subjects, 3 bipolar EEG channels (C3, Cz, C4) + 3 EOG channels, 250 Hz.
DOI: 10.1109/TNSRE.2007.906956
License: CC-BY-ND-4.0
Investigators: R. Leeb, C. Brunner, G. R. Müller-Putz, A. Schlögl, G. Pfurtscheller, F. Lee, C. Keinrath, R. Scherer, H. Bischof
Senior author: G. Pfurtscheller
Institution: Graz University of Technology
Department: Institute for Knowledge Discovery
Country: AT
Repository: BNCI Horizon
Data URL: http://biosig.sourceforge.net/
Publication year: 2007
Keywords: brain-computer interface, BCI, electroencephalogram, EEG, motor imagery, BCI competition, smiley feedback

External Links

Source: http://biosig.sourceforge.net/

Abstract

BCI Competition 2008 Graz data set B. EEG data from 9 subjects performing two-class motor imagery (left hand vs right hand). Two screening sessions without feedback (120 trials each) and three feedback sessions with smiley feedback (160 trials each). Three bipolar EEG channels (C3, Cz, C4) and three EOG channels recorded at 250 Hz.

Methodology

Subjects performed kinesthetic motor imagery of left or right hand movements. Two screening sessions (01T, 02T) without feedback: 6 runs x 20 trials = 120 trials per session. Three feedback sessions (03T, 04E, 05E) with smiley feedback: 4 runs x 40 trials (20 per class) = 160 trials per session. Screening trials: fixation cross + beep at t=0, arrow cue at ~t=2 for 1.25s, imagery for 4s, break. Feedback trials: smiley at t=0, beep at t=2, cue from t=3 to t=7.5 with continuous smiley feedback. Three bipolar EEG channels (C3, Cz, C4) plus three monopolar EOG channels recorded at 250 Hz with 0.5-100 Hz bandpass and 50 Hz notch filter. EEG ground at Fz, EOG reference at left mastoid. Amplifier: g.tec. Software: rtsBCI (MATLAB/Simulink).

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_004 was previously named BNCI2014004. BNCI2014004 will be removed in version 1.1. .. versionadded:: 0.4.0 This dataset is commonly referred to as “BCI Competition IV Dataset 2b”. It is widely used for binary motor imagery classification tasks.

See Also BNCI2014_001 : 4-class motor imagery (Dataset 2a) BNCI2014_002 : 2-class motor imagery with Laplacian derivations 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) NeuroTechX/moabb

§ 03Cohort · Participants

Cohort#

Dataset Statistics#

Age distribution by gender (n=1, range 21–21 yr, mean 21.0 yr)

20

Female · 1

Sex composition

Female
1

HandednessRight · 1

Channel counts: 3 ch (n=5 recordings)

Sampling frequencies: 250.0 Hz (n=5 recordings)

Total recording duration: 2 h 51 min

§ 04Signal · Electrodes & trace

Signal · Electrodes & live trace#

Fig. 01 Signal & montage 3 ch · EEG · 250 Hz · 1 subjects, 5 recordings

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

Showing one representative recording out of 1 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.

No scalp electrode layout is currently indexed for this dataset. Once the eegdash montage registry ingests it, the interactive viewer will appear here automatically.

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.NM000135(cache_dir, query=None, s3_bucket=None, **kwargs)

Bases: EEGDashDataset

Author (year)Leeb2014

Canonical—

Importable asNM000135 · Leeb2014

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

class eegdash.dataset.NM000135(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#

BNCI 2014-004 Motor Imagery dataset

Study:: nm000135 (NeMAR)
Author (year):: Leeb2014
Canonical:: —

Also importable as: NM000135, Leeb2014.

Modality: eeg; Experiment type: Motor; Subject type: Healthy. Subjects: 1; 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/nm000135 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=nm000135 DOI: https://doi.org/10.82901/nemar.nm000135

Examples

>>> from eegdash.dataset import NM000135
>>> dataset = NM000135(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 — NM000135.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 nm000135 to reproduce the tutorial on this dataset.

Citation

R. Leeb, C. Brunner, G. R. Müller-Putz, A. Schlögl, G. Pfurtscheller, … (2019). BNCI 2014-004 Motor Imagery dataset. 10.82901/nemar.nm000135

Provenance

¹Contributed to nemar in BIDS format.

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

³Persistent identifier: 10.82901/nemar.nm000135.

Related & sibling datasets

NM000137EEG · 7 subj NM000146EEG · 10 subj NM000138EEG · 8 subj NM000243EEG · 15 subj NM000143EEG · 5 subj

+ 1 more — see See Also below →

BIDS

BIDS 1.9.0

Sidecars

events · events.json · channels · eeg.json

Provenance

CC-BY-ND-4.0 · 10.82901/nemar.nm000135

Machine-readable

schema.org/Dataset · Croissant

Mirrors

OpenNeuro · NEMAR · HF org

Dataset ID	`NM000135`
Title	BNCI 2014-004 Motor Imagery dataset
Author (year)	`Leeb2014`
Canonical	—
Importable as	`NM000135`, `Leeb2014`
Year	2019
Authors	Leeb, C. Brunner, G. R. Müller-Putz, A. Schlögl, G. Pfurtscheller, F. Lee, C. Keinrath, R. Scherer, H. Bischof
License	CC-BY-ND-4.0
Citation / DOI	10.82901/nemar.nm000135
Source links	OpenNeuro \| NeMAR \| Source URL

NM000135: eeg dataset, 1 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#