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%)

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},
}

About This Dataset#

DOI

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

View full README

DOI

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

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	10.82901/nemar.nm000151
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

Electrode Layout#

Electrode layout — EEG · 32 sensors — 32 channels

Dataset Statistics#

Channel counts: 32 ch (n=46 recordings)

Sampling frequencies: 1000.0 Hz (n=46 recordings)

Total recording duration: 9 h 54 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 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 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)