EEGdash›NeMAR›NM000138

Iss. 138 · 8 subjects · 8 recordings · CC-BY-SA-4.0

Dataset Brief · Alex Motor Imagery dataset

NM000138: eeg dataset, 8 subjects#

Alex Motor Imagery dataset

Citation: Alexandre Barachant (2019). Alex Motor Imagery dataset. 10.82901/nemar.nm000138

8-participant EEG dataset — Alex Motor Imagery dataset.

Data & curation Alexandre Barachant
Year 2019 · Distributed via NeMAR

EEG · 16 ch512 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 NM000138

dataset = NM000138(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 = NM000138(cache_dir="./data", subject="01")

Advanced query

dataset = NM000138(
    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{nm000138,
  title = {Alex Motor Imagery dataset},
  author = {Alexandre Barachant},
  doi = {10.82901/nemar.nm000138},
  url = {https://doi.org/10.82901/nemar.nm000138},
}

§ 02Study · The README

About This Dataset#

Alex Motor Imagery dataset.

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

DOI

Alex Motor Imagery dataset

right_hand

View full README

DOI

Alex 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

rest

├─ Sensory-event
├─ Experimental-stimulus
├─ Visual-presentation
└─ Rest

Paradigm-Specific Parameters

Detected paradigm: motor_imagery
Imagery tasks: right_hand, feet, rest
Cue duration: 1.0 s
Imagery duration: 3.0 s

Data Structure

Trials: 60
Trials per class: right_hand=20, feet=20, rest=20
Trials context: 20 trials per class, 3 second duration each

Preprocessing

Re-reference: earlobe

Signal Processing

Classifiers: LDA, SVM, MDM, Riemannian, kNN, Naive Bayes, Logistic Regression
Feature extraction: CSP, FBCSP, ERD, ERS, PSD, Covariance/Riemannian, AR, ICA
Frequency bands: alpha=[8.0, 12.0] Hz; mu=[8.0, 12.0] Hz
Spatial filters: CSP, Geodesic filtering

Cross-Validation

Method: cross-validation
Evaluation type: within_session

BCI Application

Applications: motor_control
Environment: laboratory
Online feedback: False

Tags

Pathology: Healthy
Modality: Motor
Type: Research

Documentation

Description: Motor imagery dataset from the PhD dissertation of A. Barachant. Contains EEG recordings from 8 subjects performing motor imagination tasks (right hand, feet, or rest). Used to validate robust control of an effector via asynchronous EEG-based brain-machine interface.
DOI: 10.5281/zenodo.806022
Associated paper DOI: tel-01196752v1
License: CC-BY-SA-4.0
Investigators: Alexandre Barachant
Senior author: Alexandre Barachant
Contact: alexandre.barachant@gmail.com
Institution: Université de Grenoble
Department: Laboratoire Électronique et système pour la santé CEA-LETI
Address: CEA-LETI Grenoble, France
Country: France
Repository: Zenodo
Data URL: https://zenodo.org/record/806023
Publication year: 2012
Keywords: brain-computer interface, motor imagery, EEG, Riemannian geometry, asynchronous BCI, brain-switch, covariance matrices, Common Spatial Pattern

Abstract

Motor imagery dataset from the PhD thesis on robust control of an effector via asynchronous EEG brain-machine interface (Barachant, 2012). This shared dataset corresponds to Step B (cue-based imagery without feedback) of the Brain Switch campaign. Contains recordings from 8 subjects performing 3 motor imagery tasks (right hand, feet, rest) with 20 trials per class.

Methodology

Cue-based paradigm without feedback (Step B of Brain Switch campaign). EEG recorded at 512 Hz with 16 active electrodes using a g.tec g.USBamp amplifier. Reference electrode placed on the ear. Subjects performed imagined movements following visual cues: right hand, feet, and rest, 20 trials per class, 3 seconds each. Recorded in standard office conditions (not shielded laboratory). Software: Matlab/Simulink with g.tec drivers.

References

Barachant, A., 2012. Commande robuste d’un effecteur par une interface cerveau machine EEG asynchrone (Doctoral dissertation, Université de Grenoble). https://tel.archives-ouvertes.fr/tel-01196752 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#

Channel counts: 16 ch (n=8 recordings)

Sampling frequencies: 512.0 Hz (n=8 recordings)

Total recording duration: 1 h 6 min

§ 04Signal · Electrodes & trace

Signal · Electrodes & live trace#

Fig. 01 Signal & montage 16 ch · EEG · 512 Hz · 8 subjects, 8 recordings

Live trace viewer — sub-6 · ses-0 · task-imagery · run-0

Showing one representative recording out of 8 subjects and 8 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 · 16 sensors — 16 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.NM000138(cache_dir, query=None, s3_bucket=None, **kwargs)

Bases: EEGDashDataset

Author (year)Barachant2012

Canonical—

Importable asNM000138 · Barachant2012

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

class eegdash.dataset.NM000138(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#

Alex Motor Imagery dataset

Study:: nm000138 (NeMAR)
Author (year):: Barachant2012
Canonical:: —

Also importable as: NM000138, Barachant2012.

Modality: eeg; Experiment type: Motor; Subject type: Healthy. Subjects: 8; recordings: 8; 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/nm000138 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=nm000138 DOI: https://doi.org/10.82901/nemar.nm000138

Examples

>>> from eegdash.dataset import NM000138
>>> dataset = NM000138(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 — NM000138.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 nm000138 to reproduce the tutorial on this dataset.

Citation

Alexandre Barachant (2019). Alex Motor Imagery dataset. 10.82901/nemar.nm000138

Provenance

¹Contributed to nemar in BIDS format.

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

³Persistent identifier: 10.82901/nemar.nm000138.

Related & sibling datasets

NM000140EEG · 12 subj NM000171EEG · 14 subj NM000145EEG · 10 subj NM000137EEG · 7 subj NM000162EEG · 20 subj

+ 1 more — see See Also below →

BIDS

BIDS 1.9.0

Sidecars

events · events.json · channels · eeg.json

Provenance

CC-BY-SA-4.0 · 10.82901/nemar.nm000138

Machine-readable

schema.org/Dataset · Croissant

Mirrors

OpenNeuro · NEMAR · HF org

Dataset ID	`NM000138`
Title	Alex Motor Imagery dataset
Author (year)	`Barachant2012`
Canonical	—
Importable as	`NM000138`, `Barachant2012`
Year	2019
Authors	Alexandre Barachant
License	CC-BY-SA-4.0
Citation / DOI	10.82901/nemar.nm000138
Source links	OpenNeuro \| NeMAR \| Source URL

NM000138: eeg dataset, 8 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#