NM000230: eeg dataset, 30 subjects#

Lower-limb MI dataset for knee pain patients from Zuo et al. 2025

Access recordings and metadata through EEGDash.

Citation: Chongwen Zuo, Yi Yin, Haochong Wang, Zhiyang Zheng, Xiaoyan Ma, Yuan Yang, Jue Wang, Shan Wang, Zi-gang Huang, Chaoqun Ye (2025). Lower-limb MI dataset for knee pain patients from Zuo et al. 2025.

Modality: eeg Subjects: 30 Recordings: 118 License: CC-BY-4.0 Source: nemar

Metadata: Complete (90%)

Quickstart#

Get Started

Install

pip install eegdash

Access the data

from eegdash.dataset import NM000230

dataset = NM000230(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 = NM000230(cache_dir="./data", subject="01")

Advanced query

dataset = NM000230(
    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{nm000230,
  title = {Lower-limb MI dataset for knee pain patients from Zuo et al. 2025},
  author = {Chongwen Zuo and Yi Yin and Haochong Wang and Zhiyang Zheng and Xiaoyan Ma and Yuan Yang and Jue Wang and Shan Wang and Zi-gang Huang and Chaoqun Ye},
}

About This Dataset#

Lower-limb MI dataset for knee pain patients from Zuo et al. 2025

Lower-limb MI dataset for knee pain patients from Zuo et al. 2025.

Dataset Overview

Code: Zuo2025
Paradigm: imagery
DOI: 10.1038/s41597-025-05767-2

View full README

Lower-limb MI dataset for knee pain patients from Zuo et al. 2025

Lower-limb MI dataset for knee pain patients from Zuo et al. 2025.

Dataset Overview

Code: Zuo2025
Paradigm: imagery
DOI: 10.1038/s41597-025-05767-2
Subjects: 30
Sessions per subject: 5
Events: left_leg=1, right_leg=2
Trial interval: [0, 4] s
File format: MAT

Acquisition

Sampling rate: 500.0 Hz
Number of channels: 30
Channel types: eeg=30
Channel names: Fp1, Fp2, Fz, F3, F4, F7, F8, FCz, FC3, FC4, FT7, FT8, Cz, C3, C4, T3, T4, CPz, CP3, CP4, TP7, TP8, Pz, P3, P4, T5, T6, Oz, O1, O2
Montage: standard_1005
Hardware: ZhenTec EEG system
Reference: CPz
Ground: FPz
Line frequency: 50.0 Hz

Participants

Number of subjects: 30
Health status: knee pain patients
Clinical population: knee_pain
Age: mean=33.5, min=24, max=45
Gender distribution: female=12, male=18
Species: human

Experimental Protocol

Paradigm: imagery
Number of classes: 2
Class labels: left_leg, right_leg
Trial duration: 4.0 s
Study design: 2-class lower-limb MI (left/right leg flexion/extension). 5 sessions, 100 trials per session.
Feedback type: none
Stimulus type: visual
Stimulus modalities: visual
Primary modality: visual
Synchronicity: cue-based
Mode: offline

HED Event Annotations

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

left_leg

     ├─ Sensory-event
     └─ Label/left_leg

right_leg

├─ Sensory-event
└─ Label/right_leg

Paradigm-Specific Parameters

Detected paradigm: motor_imagery
Imagery tasks: left_leg, right_leg
Imagery duration: 4.0 s

Data Structure

Trials: 500
Trials per class: left_leg=250, right_leg=250
Trials context: 5 sessions x 100 trials (50 left + 50 right)

Signal Processing

Classifiers: CSP+LDA, FBCSP+SVM, EEGNet, OTFWRGD
Feature extraction: CSP, FBCSP, deep_learning, Riemannian_geometry
Frequency bands: alpha_mu=[8.0, 15.0] Hz; beta=[15.0, 30.0] Hz
Spatial filters: CSP, FBCSP

Cross-Validation

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

BCI Application

Applications: rehabilitation
Environment: clinical
Online feedback: False

Tags

Pathology: Knee Pain
Modality: Motor
Type: Clinical, Motor Imagery

Documentation

DOI: 10.1038/s41597-025-05767-2
License: CC-BY-4.0
Investigators: Chongwen Zuo, Yi Yin, Haochong Wang, Zhiyang Zheng, Xiaoyan Ma, Yuan Yang, Jue Wang, Shan Wang, Zi-gang Huang, Chaoqun Ye
Institution: Air Force Medical Center, Beijing
Country: CN
Data URL: https://figshare.com/articles/dataset/28740260
Publication year: 2025

References

Zuo, C., Yin, Y., Wang, H., et al. (2025). Enhancing classification of a large lower-limb motor imagery EEG dataset for BCI in knee pain patients. Scientific Data, 12, 1451. https://doi.org/10.1038/s41597-025-05767-2 Notes .. versionadded:: 1.2.0 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	`NM000230`
Title	Lower-limb MI dataset for knee pain patients from Zuo et al. 2025
Author (year)	`Zuo2025`
Canonical	—
Importable as	`NM000230`, `Zuo2025`
Year	2025
Authors	Chongwen Zuo, Yi Yin, Haochong Wang, Zhiyang Zheng, Xiaoyan Ma, Yuan Yang, Jue Wang, Shan Wang, Zi-gang Huang, Chaoqun Ye
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: 30
Recordings: 118
Tasks: 1

Channels & sampling rate

Channels: 30
Sampling rate (Hz): 500.0
Duration (hours): 38.07771222222222

Electrode Layout#

Electrode layout — EEG · 30 sensors — 30 channels

Dataset Statistics#

Age distribution (n=30, range 33–33 yr)

30

Channel counts: 30 ch (n=118 recordings)

Sampling frequencies: 500.0 Hz (n=118 recordings)

Total recording duration: 38 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 NM000230 class to access this dataset programmatically.

class eegdash.dataset.NM000230(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#

Bases: EEGDashDataset

Lower-limb MI dataset for knee pain patients from Zuo et al. 2025

Study:: nm000230 (NeMAR)
Author (year):: Zuo2025
Canonical:: —

Also importable as: NM000230, Zuo2025.

Modality: eeg; Experiment type: Motor; Subject type: Other. Subjects: 30; recordings: 118; 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/nm000230 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=nm000230

Examples

>>> from eegdash.dataset import NM000230
>>> dataset = NM000230(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)