EEGdash›NeMAR›NM000140

Iss. 140 · 12 subjects · 28 recordings · CC-BY-NC-ND-4.0

Dataset Brief · BNCI 2015-001 Motor Imagery dataset

NM000140: eeg dataset, 12 subjects#

Name: BNCI 2015-001 Motor Imagery dataset
Published: 2012-01-01
License: CC-BY-NC-ND-4.0

BNCI 2015-001 Motor Imagery dataset

Citation: Josef Faller, Carmen Vidaurre, Teodoro Solis-Escalante, Christa Neuper, Reinhold Scherer (2012). BNCI 2015-001 Motor Imagery dataset. 10.82901/nemar.nm000140

12-participant EEG dataset — BNCI 2015-001 Motor Imagery dataset.

Data & curation Josef Faller · Carmen Vidaurre · Teodoro Solis-Escalante · Christa Neuper · Reinhold Scherer
Year 2012 · Distributed via NeMAR
Funding FP7 Framework EU Research Project BrainAble (No. 247447)

EEG · 13 ch512 HzBIDS 1.9.0Task · imagery3 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 NM000140

dataset = NM000140(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 = NM000140(cache_dir="./data", subject="01")

Advanced query

dataset = NM000140(
    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{nm000140,
  title = {BNCI 2015-001 Motor Imagery dataset},
  author = {Josef Faller and Carmen Vidaurre and Teodoro Solis-Escalante and Christa Neuper and Reinhold Scherer},
  doi = {10.82901/nemar.nm000140},
  url = {https://doi.org/10.82901/nemar.nm000140},
}

§ 02Study · The README

About This Dataset#

BNCI 2015-001 Motor Imagery dataset.

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

DOI

BNCI 2015-001 Motor Imagery dataset

right_hand

View full README

DOI

BNCI 2015-001 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_palmar_grip, both_feet_plantar_extension
Cue duration: 1.25 s
Imagery duration: 4.0 s

Data Structure

Trials: 200
Trials per class: right_hand=100, feet=100
Trials context: per_session

Preprocessing

Data state: filtered
Preprocessing applied: True
Steps: bandpass filter, notch filter
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
Re-reference: car

Signal Processing

Classifiers: LDA
Feature extraction: logarithmic bandpower, CSP
Frequency bands: alpha=[10, 13] Hz; beta=[16, 24] Hz

Cross-Validation

Method: leave-one-out
Evaluation type: cross_session

Performance (Original Study)

Accuracy: 80.0%

BCI Application

Applications: communication, control
Online feedback: True

Tags

Pathology: Healthy
Modality: Motor
Type: Motor

Documentation

DOI: 10.1109/tnsre.2012.2189584
License: CC-BY-NC-ND-4.0
Investigators: Josef Faller, Carmen Vidaurre, Teodoro Solis-Escalante, Christa Neuper, Reinhold Scherer
Senior author: Reinhold Scherer
Contact: josef.faller@tugraz.at; christa.neuper@uni-graz.at; carmen.vidaurre@tu-berlin.de
Institution: Graz University of Technology
Department: Institute of Knowledge Discovery
Address: 8010 Graz, Austria
Country: Austria
Repository: BNCI Horizon
Publication year: 2012
Funding: FP7 Framework EU Research Project BrainAble (No. 247447)

References

Faller, J., Vidaurre, C., Solis-Escalante, T., Neuper, C., & Scherer, R. (2012). Autocalibration and recurrent adaptation: Towards a plug and play online ERD-BCI. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 20(3), 313-319. https://doi.org/10.1109/tnsre.2012.2189584 Notes .. note:: BNCI2015_001 was previously named BNCI2015001. BNCI2015001 will be removed in version 1.1. .. versionadded:: 0.4.0 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=12, range 25–25 yr, mean 24.0 yr)

20

Other · 12

Channel counts: 13 ch (n=28 recordings)

Sampling frequencies: 512.0 Hz (n=28 recordings)

Total recording duration: 16 h 41 min

§ 04Signal · Electrodes & trace

Signal · Electrodes & live trace#

Fig. 01 Signal & montage 13 ch · EEG · 512 Hz · 12 subjects, 28 recordings

Live trace viewer — sub-12 · ses-1B · task-imagery · run-0

Showing one representative recording out of 12 subjects and 28 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 · 13 sensors — 13 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.NM000140(cache_dir, query=None, s3_bucket=None, **kwargs)

Bases: EEGDashDataset

Author (year)Faller2015

Canonical—

Importable asNM000140 · Faller2015

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

class eegdash.dataset.NM000140(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#

BNCI 2015-001 Motor Imagery dataset

Study:: nm000140 (NeMAR)
Author (year):: Faller2015
Canonical:: —

Also importable as: NM000140, Faller2015.

Modality: eeg; Experiment type: Motor; Subject type: Healthy. Subjects: 12; recordings: 28; 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/nm000140 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=nm000140 DOI: https://doi.org/10.82901/nemar.nm000140

Examples

>>> from eegdash.dataset import NM000140
>>> dataset = NM000140(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 — NM000140.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 nm000140 to reproduce the tutorial on this dataset.

Citation

Josef Faller, Carmen Vidaurre, Teodoro Solis-Escalante, Christa Neuper, Reinhold Scherer (2012). BNCI 2015-001 Motor Imagery dataset. 10.82901/nemar.nm000140

Provenance

¹Contributed to nemar in BIDS format.

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

³Persistent identifier: 10.82901/nemar.nm000140.

Related & sibling datasets

NM000138EEG · 8 subj NM000171EEG · 14 subj NM000161EEG · 20 subj NM000162EEG · 20 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-NC-ND-4.0 · 10.82901/nemar.nm000140

Machine-readable

schema.org/Dataset · Croissant

Mirrors

OpenNeuro · NEMAR · HF org

Dataset ID	`NM000140`
Title	BNCI 2015-001 Motor Imagery dataset
Author (year)	`Faller2015`
Canonical	—
Importable as	`NM000140`, `Faller2015`
Year	2012
Authors	Josef Faller, Carmen Vidaurre, Teodoro Solis-Escalante, Christa Neuper, Reinhold Scherer
License	CC-BY-NC-ND-4.0
Citation / DOI	10.82901/nemar.nm000140
Source links	OpenNeuro \| NeMAR \| Source URL

NM000140: eeg dataset, 12 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#