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.

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

Metadata: Complete (90%)

Quickstart#

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)

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'])

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},
}

About This Dataset#

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

View full README

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

Dataset Information#

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

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: 12

  • Recordings: 46

  • Tasks: 1

Channels & sampling rate

  • Channels: 32

  • Sampling rate (Hz): 1000.0

  • Duration (hours): 9.901242777777778

Tags

  • Pathology: Healthy

  • Modality: Visual

  • Type: Motor

Files & format

  • Size on disk: 3.2 GB

  • File count: 46

  • Format: BIDS

License & citation

  • License: CC0-1.0

  • DOI: —

Provenance

API Reference#

Use the NM000151 class to access this dataset programmatically.

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

Bases: EEGDashDataset

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

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, 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#