NM000138: eeg dataset, 8 subjects#

Alex Motor Imagery dataset

Access recordings and metadata through EEGDash.

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

Modality: eeg Subjects: 8 Recordings: 8 License: CC-BY-SA-4.0 Source: nemar

Metadata: Complete (100%)

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},
}

About This Dataset#

DOI

Alex Motor Imagery dataset

Alex Motor Imagery dataset.

Dataset Overview

Code: AlexandreMotorImagery
Paradigm: imagery

View full README

DOI

Alex Motor Imagery dataset

Alex Motor Imagery dataset.

Dataset Overview

Code: AlexandreMotorImagery
Paradigm: imagery
DOI: 10.5281/zenodo.806022
Subjects: 8
Sessions per subject: 1
Events: right_hand=2, feet=3, rest=4
Trial interval: [0, 3] s
File format: fif
Data preprocessed: True

Acquisition

Sampling rate: 512.0 Hz
Number of channels: 16
Channel types: eeg=16
Channel names: Fpz, F7, F3, Fz, F4, F8, T7, C3, Cz, C4, T8, P7, P3, Pz, P4, P8
Montage: standard_1005
Hardware: g.tec g.USBamp
Software: Matlab/Simulink
Reference: earlobe
Sensor type: EEG
Line frequency: 50.0 Hz

Participants

Number of subjects: 8
Health status: healthy
Species: human

Experimental Protocol

Paradigm: imagery
Number of classes: 3
Class labels: right_hand, feet, rest
Trial duration: 3.0 s
Study design: Cue-based motor imagery paradigm (Step B of Brain Switch campaign) for familiarization and algorithm development
Feedback type: none
Stimulus type: visual cue
Stimulus modalities: visual, auditory
Primary modality: visual
Synchronicity: synchronous
Mode: offline
Instructions: Cue-based paradigm without feedback. Subjects perform 20 imagined movements per class (right hand, feet, rest) following a visual cue, lasting 3 seconds each. Total duration approximately 10 minutes.

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

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

Dataset Information#

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

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: 8
Recordings: 8
Tasks: 1

Channels & sampling rate

Channels: 16
Sampling rate (Hz): 512.0
Duration (hours): 1.1037152777777777

Electrode Layout#

Electrode layout — EEG · 16 sensors — 16 channels

Dataset Statistics#

Channel counts: 16 ch (n=8 recordings)

Sampling frequencies: 512.0 Hz (n=8 recordings)

Total recording duration: 1 h 6 min

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

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.

Files: —

Size: —

Subjects: —

Click to load file structure…

API Reference#

Use the NM000138 class to access this dataset programmatically.

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

Bases: EEGDashDataset

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)