NM000138: eeg dataset, 8 subjects#

Alex Motor Imagery dataset

Access recordings and metadata through EEGDash.

Citation: Alexandre Barachant (2019). Alex Motor Imagery dataset.

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

Metadata: Complete (90%)

Quickstart#

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)

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'])

If you use this dataset in your research, please cite the original authors.

BibTeX

@dataset{nm000138,
  title = {Alex Motor Imagery dataset},
  author = {Alexandre Barachant},
}

About This Dataset#

Alex Motor Imagery dataset

Alex Motor Imagery dataset.

Dataset Overview

  • Code: AlexandreMotorImagery

  • Paradigm: imagery

  • DOI: 10.5281/zenodo.806022

View full README

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) https://github.com/NeuroTechX/moabb

Dataset Information#

Dataset ID

NM000138

Title

Alex Motor Imagery dataset

Author (year)

Barachant2012

Canonical

AlexMI, AlexMotorImagery, AlexandreMotorImagery

Importable as

NM000138, Barachant2012, AlexMI, AlexMotorImagery, AlexandreMotorImagery

Year

2019

Authors

Alexandre Barachant

License

CC-BY-SA-4.0

Citation / DOI

Unknown

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

Tags

  • Pathology: Healthy

  • Modality: Visual

  • Type: Motor

Files & format

  • Size on disk: 99.7 MB

  • File count: 8

  • Format: BIDS

License & citation

  • License: CC-BY-SA-4.0

  • DOI: —

Provenance

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:

AlexMI, AlexMotorImagery, AlexandreMotorImagery

Also importable as: NM000138, Barachant2012, AlexMI, AlexMotorImagery, AlexandreMotorImagery.

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

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, overwrite=False)[source]#

Save the dataset to disk.

Parameters:

  • path (str or Path) – Destination file path.

  • overwrite (bool, default False) – If True, overwrite existing file.

Return type:

None

See Also#