EEGdash›NeMAR›NM000151

Iss. 151 · 12 subjects · 46 recordings · CC0-1.0

Dataset Brief · Motor imagery dataset for three imaginary states of the same…

NM000151: eeg dataset, 12 subjects#

Motor imagery dataset for three imaginary states of the same upper extremity

Access recordings and metadata through EEGDash.

Citation: Mojgan Tavakolan, Zack Frehlick, Xinyi Yong, Carlo Menon (2019). Motor imagery dataset for three imaginary states of the same upper extremity. 10.82901/nemar.nm000151

Modality: eeg Subjects: 12 Recordings: 46 License: CC0-1.0 Source: nemar

Metadata: Complete (100%)

12-participant EEG dataset — Motor imagery dataset for three imaginary states of the same upper extremity.

Data & curation Mojgan Tavakolan · Zack Frehlick · Xinyi Yong · Carlo Menon
Year 2019 · Distributed via NeMAR

EEG · 32 ch1000 HzBIDS 1.9.0Task · imagery4 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 NM000151

dataset = NM000151(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 = NM000151(cache_dir="./data", subject="01")

Advanced query

dataset = NM000151(
    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{nm000151,
  title = {Motor imagery dataset for three imaginary states of the same upper extremity},
  author = {Mojgan Tavakolan and Zack Frehlick and Xinyi Yong and Carlo Menon},
  doi = {10.82901/nemar.nm000151},
  url = {https://doi.org/10.82901/nemar.nm000151},
}

§ 02Study · The README

About This Dataset#

Motor imagery dataset for three imaginary states of the same upper extremity.

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

DOI

Motor imagery dataset for three imaginary states of the same upper extremity

rest

View full README

DOI

Motor imagery dataset for three imaginary states of the same upper extremity

rest

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

right_hand

     ├─ Sensory-event, Experimental-stimulus, Visual-presentation
     └─ Agent-action
        └─ Imagine
           ├─ Move
           └─ Right, Hand

right_elbow_flexion

├─ Sensory-event, Experimental-stimulus, Visual-presentation
└─ Agent-action
   └─ Imagine
      ├─ Flex
      └─ Right, Elbow

Paradigm-Specific Parameters

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

Data Structure

Trials: 2880
Trials per class: rest=20, right_hand=20, right_elbow_flexion=20
Trials context: 12 subjects x 4 sessions x 60 trials (20 per class)

Preprocessing

Data state: continuous

Signal Processing

Classifiers: SVM-RBF
Feature extraction: autoregressive_coefficients, waveform_length, root_mean_square
Frequency bands: bandpass=[6.0, 35.0] Hz

Cross-Validation

Method: 10x10-fold
Folds: 10
Evaluation type: within_subject

BCI Application

Applications: motor_control, rehabilitation
Environment: laboratory
Online feedback: False

Tags

Pathology: Healthy
Modality: Motor
Type: Research

Documentation

DOI: 10.1371/journal.pone.0174161
License: CC0-1.0
Investigators: Mojgan Tavakolan, Zack Frehlick, Xinyi Yong, Carlo Menon
Senior author: Carlo Menon
Institution: Simon Fraser University
Department: MENRVA Research Group, Schools of Mechatronic Systems Engineering and Engineering Science
Country: CA
Repository: Zenodo
Data URL: https://zenodo.org/records/18967205
Publication year: 2017
Ethics approval: Simon Fraser University Office of Research Ethics
Keywords: motor imagery, EEG, upper extremity, same limb, time-domain features, SVM, BCI

References

M. Tavakolan, Z. Frehlick, X. Yong, and C. Menon, “Classifying three imaginary states of the same upper extremity using time-domain features,” PLoS ONE, vol. 12, no. 3, e0174161, 2017. DOI: 10.1371/journal.pone.0174161 M. Tavakolan, Z. Frehlick, X. Yong, and C. Menon, “Data from: Classifying three imaginary states of the same upper extremity using time-domain features,” Dryad, 2017. DOI: 10.5061/dryad.6qs86 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 1.5.0 (Mother of All BCI Benchmarks) NeuroTechX/moabb

NEMAR Metadata#

[![DOI](https://img.shields.io/badge/DOI-10.82901%2Fnemar.nm000151-blue)](https://doi.org/10.82901/nemar.nm000151) # Motor imagery dataset for three imaginary states of the same upper extremity Motor imagery dataset for three imaginary states of the same upper extremity. ## Dataset Overview - Code: Tavakolan2017 - Paradigm: imagery - DOI: 10.1371/journal.pone.0174161 - Subjects: 12 - Sessions per subject: 4 - Events: rest=1, right_hand=2, right_elbow_flexion=3 - Trial interval: [0, 3] s - File format: BCI2000 ## Acquisition - Sampling rate: 1000.0 Hz - Number of channels: 32 - Channel types: eeg=32 - Montage: GSN-HydroCel-32 - Hardware: EGI Geodesic Net Amps 400 series - Reference: Cz - Sensor type: Ag/AgCl sponge - Line frequency: 60.0 Hz - Online filters: {‘bandpass’: [0.1, 100]} - Impedance threshold: 50 kOhm ## Participants - Number of subjects: 12 - Health status: healthy - Species: human ## Experimental Protocol - Paradigm: imagery - Number of classes: 3 - Class labels: rest, right_hand, right_elbow_flexion - Trial duration: 3.0 s - Study design: Three-class motor imagery of the same upper extremity: rest, grasping (MI-GRASP), and elbow flexion (MI-ELBOW). 20 trials per class per session, 4 sessions per subject. - Feedback type: none - Stimulus type: visual cue - Stimulus modalities: visual - Primary modality: visual - Synchronicity: synchronous - Mode: offline - Instructions: REST: relax without movement. MI-GRASP: imagine opening and closing all fingers to grab an object. MI-ELBOW: imagine moving the forearm up and down. ## HED Event Annotations Schema: HED 8.4.0 | Browse: https://www.hedtags.org/hed-schema-browser ```

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

right_hand
├─ Sensory-event, Experimental-stimulus, Visual-presentation └─ Agent-action

└─ Imagine
├─ Move └─ Right, Hand

right_elbow_flexion
├─ Sensory-event, Experimental-stimulus, Visual-presentation └─ Agent-action

└─ Imagine
├─ Flex └─ Right, Elbow

``` ## Paradigm-Specific Parameters - Detected paradigm: motor_imagery - Imagery tasks: rest, right_hand, right_elbow_flexion - Cue duration: 3.0 s - Imagery duration: 3.0 s ## Data Structure - Trials: 2880 - Trials per class: rest=20, right_hand=20, right_elbow_flexion=20 - Trials context: 12 subjects x 4 sessions x 60 trials (20 per class) ## Preprocessing - Data state: continuous ## Signal Processing - Classifiers: SVM-RBF - Feature extraction: autoregressive_coefficients, waveform_length, root_mean_square - Frequency bands: bandpass=[6.0, 35.0] Hz ## Cross-Validation - Method: 10x10-fold - Folds: 10 - Evaluation type: within_subject ## BCI Application - Applications: motor_control, rehabilitation - Environment: laboratory - Online feedback: False ## Tags - Pathology: Healthy - Modality: Motor - Type: Research ## Documentation - DOI: 10.1371/journal.pone.0174161 - License: CC0-1.0 - Investigators: Mojgan Tavakolan, Zack Frehlick, Xinyi Yong, Carlo Menon - Senior author: Carlo Menon - Institution: Simon Fraser University - Department: MENRVA Research Group, Schools of Mechatronic Systems Engineering and Engineering Science - Country: CA - Repository: Zenodo - Data URL: https://zenodo.org/records/18967205 - Publication year: 2017 - Ethics approval: Simon Fraser University Office of Research Ethics - Keywords: motor imagery, EEG, upper extremity, same limb, time-domain features, SVM, BCI ## References M. Tavakolan, Z. Frehlick, X. Yong, and C. Menon, “Classifying three imaginary states of the same upper extremity using time-domain features,” PLoS ONE, vol. 12, no. 3, e0174161, 2017. DOI: 10.1371/journal.pone.0174161 M. Tavakolan, Z. Frehlick, X. Yong, and C. Menon, “Data from: Classifying three imaginary states of the same upper extremity using time-domain features,” Dryad, 2017. DOI: 10.5061/dryad.6qs86 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 1.5.0 (Mother of All BCI Benchmarks) NeuroTechX/moabb

License: CC0-1.0

Authors:

Mojgan Tavakolan
Zack Frehlick
Xinyi Yong
Carlo Menon

Versions:

Version	DOI	Released
`current`	10.82901/nemar.nm000151

§ 03Cohort · Participants

Cohort#

Dataset Statistics#

Channel counts: 32 ch (n=46 recordings)

Sampling frequencies: 1000.0 Hz (n=46 recordings)

Total recording duration: 9 h 54 min

§ 04Signal · Electrodes & trace

Signal · Electrodes & live trace#

Fig. 01 Signal & montage 32 ch · EEG · 1000 Hz · 12 subjects, 46 recordings

Live trace viewer — sub-12 · ses-3 · task-imagery · run-0

Showing one representative recording out of 12 subjects and 46 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 · 32 sensors — 32 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.NM000151(cache_dir, query=None, s3_bucket=None, **kwargs)

Bases: EEGDashDataset

Author (year)Tavakolan2017

Canonical—

Importable asNM000151 · Tavakolan2017

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

class eegdash.dataset.NM000151(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#

Motor imagery dataset for three imaginary states of the same upper extremity

Study:: nm000151 (NeMAR)
Author (year):: Tavakolan2017
Canonical:: —

Also importable as: NM000151, Tavakolan2017.

Modality: eeg; Experiment type: Motor; Subject type: Healthy. Subjects: 12; recordings: 46; 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/nm000151 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=nm000151 DOI: https://doi.org/10.82901/nemar.nm000151

Examples

>>> from eegdash.dataset import NM000151
>>> dataset = NM000151(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 FacePre-bundled mirror at EEGDash/nm000151 · pull with datasets.load_dataset("EEGDash/nm000151").huggingface

Croissant 1.0Machine-readable JSON-LD descriptor — NM000151.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 nm000151 to reproduce the tutorial on this dataset.

Citation

Mojgan Tavakolan, Zack Frehlick, Xinyi Yong, Carlo Menon (2019). Motor imagery dataset for three imaginary states of the same upper extremity. 10.82901/nemar.nm000151

Provenance

¹Contributed to nemar in BIDS format.

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

³Persistent identifier: 10.82901/nemar.nm000151.

Related & sibling datasets

NM000141EEG · 14 subj NM000152EEG · 12 subj NM000160EEG · 18 subj NM000142EEG · 6 subj NM000227EEG · 31 subj

+ 1 more — see See Also below →

BIDS

BIDS 1.9.0

Sidecars

events · events.json · channels · eeg.json

Provenance

CC0-1.0 · 10.82901/nemar.nm000151

Machine-readable

schema.org/Dataset · Croissant

Mirrors

OpenNeuro · NEMAR · HuggingFace · Paper

Dataset ID	`NM000151`
Title	Motor imagery dataset for three imaginary states of the same upper extremity
Author (year)	`Tavakolan2017`
Canonical	—
Importable as	`NM000151`, `Tavakolan2017`
Year	2019
Authors	Mojgan Tavakolan, Zack Frehlick, Xinyi Yong, Carlo Menon
License	CC0-1.0
Citation / DOI	10.82901/nemar.nm000151
Source links	OpenNeuro \| NeMAR

NM000151: eeg dataset, 12 subjects#

Quickstart#

About This Dataset#

NEMAR Metadata#

Cohort#

Dataset Statistics#

Signal · Electrodes & live trace#

NEMAR Processing Statistics#

Manifest#

File Explorer#

API Reference#

Citation

Provenance

Related & sibling datasets

See Also#