EEGdash›NeMAR›NM000237

Iss. 237 · 20 subjects · 833 recordings · CC-BY-4.0

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

NM000237: eeg dataset, 20 subjects#

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

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.

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

Data & curation Qing Zhou · Jiafan Lin · Lin Yao · Yueming Wang · Yan Han · Kedi Xu
Year 2021 · Distributed via NeMAR

EEG · 41 (506), 26 (327) ch500 HzBIDS 1.9.0Task · imagery7 sessionsHealthyVisualMotor

Layer 01Study

What was asked

Hypothesis, independent & dependent variables, paradigm, cohort, and the editorial caveats around what the recordings can and cannot answer.

Layer 02Signal · BIDS

What was recorded

Sidecars, channels & electrodes, coordinate system, event semantics, and quality stats from the NEMAR pipeline when available.

Layer 03Training · ML

What you can train on

Recommended access modes — MNE Raw, braindecode windows, PyTorch DataLoader — plus the targets the metadata makes addressable.

§ 01Access · Get started

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},
}

§ 02Study · The README

About This Dataset#

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

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

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

left_hand

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

View full README

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

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

§ 03Cohort · Participants

Cohort#

Dataset Statistics#

Age distribution by gender (n=20, range 23–23 yr, mean 23.0 yr)

20

Other · 20

Channel counts (ch)

2641

Sampling frequencies: 500.0 Hz (n=833 recordings)

Total recording duration: 90 h

§ 04Signal · Electrodes & trace

Signal · Electrodes & live trace#

Fig. 01 Signal & montage 41 (506), 26 (327) ch · EEG · 500 Hz · 20 subjects, 833 recordings

Live trace viewer — sub-1 · ses-0 · task-imagery · run-0

Showing one representative recording out of 20 subjects and 833 recordings in this dataset. Browse the full set on OpenNeuro; drop any other _eeg.{set,edf,bdf,vhdr} file onto the viewer (or pass ?eeg=<url>) to inspect it.

Electrode layout — EEG · 26 sensors — 26 channels

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

§ 05Manifest · BIDS tree

Manifest#

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.

Recordings—

Files—

Subjects—

Modalities—

Click to load file structure…

§ 06API · Programmatic access

API Reference#

Signature

eegdash.dataset

class

eegdash.dataset.NM000237(cache_dir, query=None, s3_bucket=None, **kwargs)

Bases: EEGDashDataset

Author (year)Zhou2021

Canonical—

Importable asNM000237 · Zhou2021

Sourceeegdash/dataset/registry.py · [source ↗]

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

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)

Parameters:

path (str) –

Directory in which subdirectories are created to store
-raw.fif | -epo.fif and .json files to.
overwrite (bool) – Whether to delete old subdirectories that will be saved to in this call.
offset (int) – If provided, the integer is added to the id of the dataset in the concat. This is useful in the setting of very large datasets, where one dataset has to be processed and saved at a time to account for its original position.

Access modesMNE → braindecode → PyTorch → ML

.rawMNE Raw object — standard tools (filter, epoch, ICA, plot_psd).mne

BaseConcatDatasetEach record is a lazy BaseDataset from braindecode — windowed via create_windows_from_events.braindecode

DataLoaderWraps the windowed dataset into a PyTorch DataLoader; supports parallel workers and on-the-fly augmentations.pytorch

Zarr cacheOptional braindecode Zarr mirror for fast resume; persisted to cache_dir.zarr

Hugging FaceNo per-dataset mirror published yet — browse the EEGDash org listing for sibling datasets. See the datasets loader API.huggingface

Croissant 1.0Machine-readable JSON-LD descriptor — NM000237.croissant.json (MLCommons schema, ingestible by PyTorch / TensorFlow / JAX).mlcommons

Examples using EEGDashcurated · start here

Find datasets with the EEGDash APIQuery the catalogue, filter by task or modality, list candidates.

Load one EEG recordingResolve a single record to an MNE Raw with channels and events.

EEG recording to PyTorch DataLoaderWrap braindecode windows in a DataLoader for model training.

Preprocess EEG and create windowsFilter, resample, epoch — and persist the windowed dataset.

Save and reload prepared dataCache a windowed dataset to disk and reattach it without recompute.

Download a dataset locallyPrefetch BIDS files to a local cache and validate the layout.

Swap any load_dataset(...) call for nm000237 to reproduce the tutorial on this dataset.

Citation

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

Provenance

¹Contributed to nemar in BIDS format.

²Curated & ingested by the EEGDash catalog; see CITATION.cff for canonical reference.

Related & sibling datasets

NM000173EEG · 15 subj NM000161EEG · 20 subj NM000167EEG · 25 subj NM000171EEG · 14 subj NM000162EEG · 20 subj

+ 1 more — see See Also below →

BIDS

BIDS 1.9.0

Sidecars

events · events.json · channels · eeg.json

Provenance

CC-BY-4.0 · DOI not on file

Machine-readable

schema.org/Dataset · Croissant

Mirrors

OpenNeuro · NEMAR · HF org

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

NM000237: eeg dataset, 20 subjects#

Quickstart#

About This Dataset#

Cohort#

Dataset Statistics#

Signal · Electrodes & live trace#

NEMAR Processing Statistics#

Manifest#

File Explorer#

API Reference#

Citation

Provenance

Related & sibling datasets

See Also#