NM000152: eeg dataset, 12 subjects

Upper-limb elbow-centered motor imagery dataset (10 classes)

Access recordings and metadata through EEGDash.

Citation: Xin Zhang, Xinyi Yong, Carlo Menon (2019). Upper-limb elbow-centered motor imagery dataset (10 classes).

Modality: eeg Subjects: 12 Recordings: 180 License: CC BY 4.0 Source: nemar

Metadata: Complete (90%)

Quickstart

Get Started

Install

pip install eegdash

Access the data

from eegdash.dataset import NM000152

dataset = NM000152(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 = NM000152(cache_dir="./data", subject="01")

Advanced query

dataset = NM000152(
    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{nm000152,
  title = {Upper-limb elbow-centered motor imagery dataset (10 classes)},
  author = {Xin Zhang and Xinyi Yong and Carlo Menon},
}

About This Dataset

Upper-limb elbow-centered motor imagery dataset (10 classes)

Upper-limb elbow-centered motor imagery dataset (10 classes).

Dataset Overview

• Code: Zhang2017

• Paradigm: imagery

• DOI: 10.1371/journal.pone.0188293

View full README

Upper-limb elbow-centered motor imagery dataset (10 classes)

Upper-limb elbow-centered motor imagery dataset (10 classes).

Dataset Overview

• Code: Zhang2017

• Paradigm: imagery

• DOI: 10.1371/journal.pone.0188293

• Subjects: 12

• Sessions per subject: 1

• Events: rest=1, elbow_flexion=2, drawer=3, soup=4, weight_lifting=5, door=6, plate_cleaning=7, combing=8, pizza_cutting=9, pick_and_place=10

• Trial interval: [0, 4] s

• Runs per session: 15

• File format: BCI2000

Acquisition

• Sampling rate: 1000.0 Hz

• Number of channels: 17

• Channel types: eeg=17

• Hardware: EGI Geodesic Net Amps 400 series (N400)

• Software: BCI2000 (Stimulus Presentation mode)

• Reference: Cz

• Ground: COM

• Sensor type: Ag/AgCl sponge

• Line frequency: 60.0 Hz

• Online filters: {‘bandpass’: [0.1, 40]}

Participants

• Number of subjects: 12

• Health status: healthy

• Age: min=20, max=33

• Gender distribution: male=10, female=2

• Handedness: {‘right’: 11, ‘left’: 1}

• BCI experience: naive

• Species: human

Experimental Protocol

• Paradigm: imagery

• Number of classes: 10

• Class labels: rest, elbow_flexion, drawer, soup, weight_lifting, door, plate_cleaning, combing, pizza_cutting, pick_and_place

• Trial duration: 5.0 s

• Study design: Upper-limb elbow-centered motor imagery with 9 goal-directed tasks plus rest. Each trial: 4-6 s cue (randomized) then 4-6 s rest (randomized).

• Feedback type: none

• Stimulus type: picture cues

• Stimulus modalities: visual

• Primary modality: visual

• Synchronicity: synchronous

• Mode: offline

• Instructions: Participants were asked to repetitively perform the kinesthetic motor imagery task displayed on the screen without actually moving.

HED Event Annotations

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

rest

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

elbow_flexion

     ├─ Sensory-event
     └─ Label/elbow_flexion

drawer

     ├─ Sensory-event
     └─ Label/drawer

soup

     ├─ Sensory-event
     └─ Label/soup

weight_lifting

     ├─ Sensory-event
     └─ Label/weight_lifting

door

     ├─ Sensory-event
     └─ Label/door

plate_cleaning

     ├─ Sensory-event
     └─ Label/plate_cleaning

combing

     ├─ Sensory-event
     └─ Label/combing

pizza_cutting

     ├─ Sensory-event
     └─ Label/pizza_cutting

pick_and_place

├─ Sensory-event
└─ Label/pick_and_place

Paradigm-Specific Parameters

• Detected paradigm: motor_imagery

• Imagery tasks: elbow_flexion, drawer, soup, weight_lifting, door, plate_cleaning, combing, pizza_cutting, pick_and_place

• Cue duration: 5.0 s

• Imagery duration: 5.0 s

Data Structure

• Trials: 330

• Trials context: 15 runs of 24 trials each (4 rest + 4 elbow + 2 each of 8 goal tasks). Total: 60 rest + 30 per MI task = 330.

Preprocessing

• Data state: raw

• Preprocessing applied: False

Signal Processing

• Classifiers: LDA, DAL

• Feature extraction: bandpower, CSP, FBCSP

• Frequency bands: bandpass=[6.0, 35.0] Hz; mu=[7.0, 13.0] Hz; beta=[13.0, 30.0] Hz

• Spatial filters: CSP, FBCSP

Cross-Validation

• Method: 5x5-fold

• Folds: 5

• 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.0188293

• License: CC BY 4.0

• Investigators: Xin Zhang, Xinyi Yong, Carlo Menon

• Senior author: Carlo Menon

• Institution: Simon Fraser University

• Department: School of Engineering Science

• Country: CA

• Repository: Figshare

• Data URL: https://doi.org/10.6084/m9.figshare.5579461.v1

• Publication year: 2017

• Keywords: motor imagery, upper limb, elbow, BCI, EEG, kinesthetic imagery

References

X. Zhang, X. Yong, and C. Menon, “Evaluating the versatility of EEG models generated from motor imagery tasks: An exploratory investigation on upper-limb elbow-centered motor imagery tasks,” PLoS ONE, vol. 12, no. 11, e0188293, 2017. DOI: 10.1371/journal.pone.0188293 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	NM000152
Title	Upper-limb elbow-centered motor imagery dataset (10 classes)
Author (year)	Zhang2017
Canonical	—
Importable as	NM000152, Zhang2017
Year	2019
Authors	Xin Zhang, Xinyi Yong, Carlo Menon
License	CC BY 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: 12

• Recordings: 180

• Tasks: 1

Channels & sampling rate

• Channels: 17

• Sampling rate (Hz): 1000.0

• Duration (hours): 9.24525

Tags

• Pathology: Healthy

• Modality: Visual

• Type: Motor

Files & format

• Size on disk: 1.6 GB

• File count: 180

• Format: BIDS

License & citation

• License: CC BY 4.0

• DOI: —

Provenance

• Source: nemar

• OpenNeuro: nm000152

• NeMAR: nm000152

API Reference

Use the NM000152 class to access this dataset programmatically.

class eegdash.dataset.NM000152(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)

Bases: EEGDashDataset

Upper-limb elbow-centered motor imagery dataset (10 classes)

Study:

nm000152 (NeMAR)

Author (year):

Zhang2017

Canonical:

—

Also importable as: NM000152, Zhang2017.

Modality: eeg; Experiment type: Motor; Subject type: Healthy. Subjects: 12; recordings: 180; 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/nm000152 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=nm000152

Examples

>>> from eegdash.dataset import NM000152
>>> dataset = NM000152(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

__init__(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)

save(path, overwrite=False)

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

• eegdash.dataset.EEGDashDataset

• eegdash.dataset

• OpenNeuro dataset page

• NeMAR dataset page

• eegdash.dataset.DS001785

• eegdash.dataset.DS001787

• eegdash.dataset.DS001810

• eegdash.dataset.DS001849

• eegdash.dataset.DS001971