NM000137: eeg dataset, 7 subjects#

Classical motor imagery dataset with left hand, right hand, and rest

Access recordings and metadata through EEGDash.

Citation: Murat Kaya, Mustafa Kemal Binli, Erkan Ozbay, Hilmi Yanar, Yuriy Mishchenko (2019). Classical motor imagery dataset with left hand, right hand, and rest. 10.82901/nemar.nm000137

Modality: eeg Subjects: 7 Recordings: 17 License: CC-BY-4.0 Source: nemar

Metadata: Complete (100%)

Quickstart#

Get Started

Install

pip install eegdash

Access the data

from eegdash.dataset import NM000137

dataset = NM000137(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 = NM000137(cache_dir="./data", subject="01")

Advanced query

dataset = NM000137(
    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{nm000137,
  title = {Classical motor imagery dataset with left hand, right hand, and rest},
  author = {Murat Kaya and Mustafa Kemal Binli and Erkan Ozbay and Hilmi Yanar and Yuriy Mishchenko},
  doi = {10.82901/nemar.nm000137},
  url = {https://doi.org/10.82901/nemar.nm000137},
}

About This Dataset#

DOI

Classical motor imagery dataset with left hand, right hand, and rest

Classical motor imagery dataset with left hand, right hand, and rest.

Dataset Overview

Code: Kaya2018
Paradigm: imagery

View full README

DOI

Classical motor imagery dataset with left hand, right hand, and rest

Classical motor imagery dataset with left hand, right hand, and rest.

Dataset Overview

Code: Kaya2018
Paradigm: imagery
DOI: 10.1038/sdata.2018.211
Subjects: 7
Sessions per subject: 1
Events: left_hand=1, right_hand=2, passive=3
Trial interval: [0, 1] s
File format: MAT

Acquisition

Sampling rate: 200.0 Hz
Number of channels: 19
Channel types: eeg=19
Channel names: Fp1, Fp2, F3, F4, C3, C4, P3, P4, O1, O2, F7, F8, T3, T4, T5, T6, Fz, Cz, Pz
Montage: standard_1020
Hardware: Nihon Kohden EEG-1200
**Reference**: System 0V (0.55*(C3+C4))
Ground: A1, A2 (earlobes)
Line frequency: 50.0 Hz

Participants

Number of subjects: 7
Health status: healthy
Age: min=20, max=35
Gender distribution: male=5, female=2

Experimental Protocol

Paradigm: imagery
Task type: left_right_hand
Number of classes: 3
Class labels: left_hand, right_hand, passive
Trial duration: 1.0 s
Study design: Classical left/right hand motor imagery with passive rest
Feedback type: none
Stimulus type: visual arrow cue
Stimulus modalities: visual
Primary modality: visual
Synchronicity: synchronous
Mode: offline

HED Event Annotations

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

left_hand

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

right_hand

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

passive

├─ Sensory-event
└─ Label/passive

Paradigm-Specific Parameters

Detected paradigm: motor_imagery
Imagery tasks: left_hand, right_hand, passive
Cue duration: 1.0 s

Data Structure

Trials context: Variable number of trials per session; 1s cue + 1.5-2.5s ITI

Preprocessing

Data state: raw

Signal Processing

Classifiers: SVM
Feature extraction: fourier_transform_amplitudes
Frequency bands: low_pass=[0.0, 5.0] Hz

Cross-Validation

Method: repeated_random_split
Folds: 5
Evaluation type: within_subject

BCI Application

Environment: lab
Online feedback: False

Tags

Pathology: healthy
Modality: motor
Type: imagery

Documentation

DOI: 10.1038/sdata.2018.211
License: CC-BY-4.0
Investigators: Murat Kaya, Mustafa Kemal Binli, Erkan Ozbay, Hilmi Yanar, Yuriy Mishchenko
Senior author: Yuriy Mishchenko
Institution: Mersin University
Country: TR
Repository: Figshare
Data URL: https://figshare.com/collections/A_large_electroencephalographic_motor_imagery_dataset_for_electroencephalographic_brain_computer_interfaces/3917698
Publication year: 2018
Keywords: EEG, motor imagery, brain-computer interface, BCI

References

M. Kaya, M. K. Binli, E. Ozbay, H. Yanar, and Y. Mishchenko, “A large electroencephalographic motor imagery dataset for electroencephalographic brain computer interfaces,” Scientific Data, vol. 5, p. 180211, 2018. DOI: 10.1038/sdata.2018.211 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) NeuroTechX/moabb

Dataset Information#

Dataset ID	`NM000137`
Title	Classical motor imagery dataset with left hand, right hand, and rest
Author (year)	`Kaya2018`
Canonical	—
Importable as	`NM000137`, `Kaya2018`
Year	2019
Authors	Murat Kaya, Mustafa Kemal Binli, Erkan Ozbay, Hilmi Yanar, Yuriy Mishchenko
License	CC-BY-4.0
Citation / DOI	10.82901/nemar.nm000137
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: 7
Recordings: 17
Tasks: 1

Channels & sampling rate

Channels: 19
Sampling rate (Hz): 200.0
Duration (hours): 15.696565277777776

Electrode Layout#

Electrode layout — EEG · 19 sensors — 19 channels

Dataset Statistics#

Channel counts: 19 ch (n=17 recordings)

Sampling frequencies: 200.0 Hz (n=17 recordings)

Total recording duration: 15 h 41 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 NM000137 class to access this dataset programmatically.

class eegdash.dataset.NM000137(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#

Bases: EEGDashDataset

Classical motor imagery dataset with left hand, right hand, and rest

Study:: nm000137 (NeMAR)
Author (year):: Kaya2018
Canonical:: —

Also importable as: NM000137, Kaya2018.

Modality: eeg; Experiment type: Motor; Subject type: Healthy. Subjects: 7; recordings: 17; 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/nm000137 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=nm000137 DOI: https://doi.org/10.82901/nemar.nm000137

Examples

>>> from eegdash.dataset import NM000137
>>> dataset = NM000137(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)