NM000237: eeg dataset, 20 subjects#

7-day motor imagery BCI EEG dataset from Zhou et al 2021

Access recordings and metadata through EEGDash.

Citation: Qing Zhou, Jiafan Lin, Lin Yao, Yueming Wang, Yan Han, Kedi Xu (2021). 7-day motor imagery BCI EEG dataset from Zhou et al 2021.

Modality: eeg Subjects: 20 Recordings: 833 License: CC-BY-4.0 Source: nemar

Metadata: Complete (90%)

Quickstart#

Get Started

Install

pip install eegdash

Access the data

from eegdash.dataset import NM000237

dataset = NM000237(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 = NM000237(cache_dir="./data", subject="01")

Advanced query

dataset = NM000237(
    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{nm000237,
  title = {7-day motor imagery BCI EEG dataset from Zhou et al 2021},
  author = {Qing Zhou and Jiafan Lin and Lin Yao and Yueming Wang and Yan Han and Kedi Xu},
}

About This Dataset#

7-day motor imagery BCI EEG dataset from Zhou et al 2021

7-day motor imagery BCI EEG dataset from Zhou et al 2021.

Dataset Overview

Code: Zhou2020
Paradigm: imagery
DOI: 10.3389/fnhum.2021.701091

View full README

7-day motor imagery BCI EEG dataset from Zhou et al 2021

7-day motor imagery BCI EEG dataset from Zhou et al 2021.

Dataset Overview

Code: Zhou2020
Paradigm: imagery
DOI: 10.3389/fnhum.2021.701091
Subjects: 20
Sessions per subject: 7
Events: left_hand=1, right_hand=2, feet=3, rest=4
Trial interval: [0, 5] s
Runs per session: 6
File format: NPZ
Data preprocessed: True

Acquisition

Sampling rate: 500.0 Hz
Number of channels: 41
Channel types: eeg=41
Channel names: F3, F1, Fz, F2, F4, FC5, FC3, FC1, FCz, FC2, FC4, FC6, C5, C3, C1, Cz, C2, C4, C6, CP5, CP3, CP1, CPz, CP2, CP4, CP6
Montage: standard_1005
Hardware: Neuroscan SynAmps2
Reference: vertex (Cz)
Ground: AFz
Sensor type: Ag/AgCl
Line frequency: 50.0 Hz
Online filters: {‘bandpass’: [0.5, 100], ‘notch_hz’: 50}

Participants

Number of subjects: 20
Health status: healthy
Age: mean=23.2, std=1.47, min=21, max=27
Gender distribution: female=9, male=11
Handedness: right-handed
BCI experience: mixed
Species: human

Experimental Protocol

Paradigm: imagery
Number of classes: 4
Class labels: left_hand, right_hand, feet, rest
Trial duration: 5.0 s
Study design: 7-day longitudinal MI-BCI study without feedback training. 4 classes: left hand, right hand, both feet, idle
Feedback type: none
Stimulus type: arrow cues
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

feet

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

rest

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

Paradigm-Specific Parameters

Detected paradigm: motor_imagery
Imagery tasks: left_hand, right_hand, feet, rest
Imagery duration: 5.0 s

Data Structure

Trials: 33600
Trials context: 20 subjects x 7 sessions x 6 runs x 40 trials = 33600

Signal Processing

Classifiers: SVM
Feature extraction: CSP
Frequency bands: classification=[8.0, 30.0] Hz
Spatial filters: CSP

Cross-Validation

Method: 10-fold
Folds: 10
Evaluation type: within_session

BCI Application

Applications: research
Environment: laboratory
Online feedback: False

Tags

Pathology: Healthy
Modality: Motor
Type: Research

Documentation

DOI: 10.3389/fnhum.2021.701091
License: CC-BY-4.0
Investigators: Qing Zhou, Jiafan Lin, Lin Yao, Yueming Wang, Yan Han, Kedi Xu
Institution: Zhejiang University
Country: CN
Repository: Zenodo
Data URL: https://zenodo.org/records/18988317
Publication year: 2021

References

Zhou, Q., Lin, J., Yao, L., Wang, Y., Han, Y., Xu, K. (2021). Relative Power Correlates With the Decoding Performance of Motor Imagery Both Across Time and Subjects. Frontiers in Human Neuroscience, 15, 701091. https://doi.org/10.3389/fnhum.2021.701091 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	`NM000237`
Title	7-day motor imagery BCI EEG dataset from Zhou et al 2021
Author (year)	`Zhou2021`
Canonical	—
Importable as	`NM000237`, `Zhou2021`
Year	2021
Authors	Qing Zhou, Jiafan Lin, Lin Yao, Yueming Wang, Yan Han, Kedi Xu
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: 20
Recordings: 833
Tasks: 1

Channels & sampling rate

Channels: 41 (506), 26 (327)
Sampling rate (Hz): 500.0
Duration (hours): 90.07259277777776

Electrode Layout#

Electrode layout — EEG · 26 sensors — 26 channels

Dataset Statistics#

Age distribution (n=20, range 23–23 yr)

20

Channel counts (ch)

2641

Sampling frequencies: 500.0 Hz (n=833 recordings)

Total recording duration: 90 h

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 NM000237 class to access this dataset programmatically.

class eegdash.dataset.NM000237(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#

Bases: EEGDashDataset

7-day motor imagery BCI EEG dataset from Zhou et al 2021

Study:: nm000237 (NeMAR)
Author (year):: Zhou2021
Canonical:: —

Also importable as: NM000237, Zhou2021.

Modality: eeg; Experiment type: Motor; Subject type: Healthy. Subjects: 20; recordings: 833; 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/nm000237 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=nm000237

Examples

>>> from eegdash.dataset import NM000237
>>> dataset = NM000237(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)