NM000348: eeg dataset, 51 subjects#

Yang et al. 2025 — A multi-day and high-quality EEG dataset for motor imagery brain-computer interface

Access recordings and metadata through EEGDash.

Citation: Banghua Yang, Fenqi Rong, Yunlong Xie, Du Li, Jiayang Zhang, Fu Li, Guangming Shi, Xiaorong Gao (2025). Yang et al. 2025 — A multi-day and high-quality EEG dataset for motor imagery brain-computer interface. 10.1038/s41597-025-04826-y

Modality: eeg Subjects: 51 Recordings: 153 License: CC-BY-4.0 Source: nemar

Metadata: Complete (100%)

Quickstart#

Get Started

Install

pip install eegdash

Access the data

from eegdash.dataset import NM000348

dataset = NM000348(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 = NM000348(cache_dir="./data", subject="01")

Advanced query

dataset = NM000348(
    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{nm000348,
  title = {Yang et al. 2025 — A multi-day and high-quality EEG dataset for motor imagery brain-computer interface},
  author = {Banghua Yang and Fenqi Rong and Yunlong Xie and Du Li and Jiayang Zhang and Fu Li and Guangming Shi and Xiaorong Gao},
  doi = {10.1038/s41597-025-04826-y},
  url = {https://doi.org/10.1038/s41597-025-04826-y},
}

About This Dataset#

Yang2025

Multi-day MI-BCI dataset (WBCIC-SHU) from Yang et al 2025.

Dataset Overview

Code: Yang2025 Paradigm: imagery DOI: 10.1038/s41597-025-04826-y

View full README

Yang2025

Multi-day MI-BCI dataset (WBCIC-SHU) from Yang et al 2025.

Dataset Overview

Code: Yang2025 Paradigm: imagery DOI: 10.1038/s41597-025-04826-y Subjects: 51 Sessions per subject: 3 Events: left_hand=1, right_hand=2 Trial interval: [1.5, 5.5] s File format: BDF

Acquisition

Sampling rate: 1000.0 Hz Number of channels: 59 Channel types: eeg=59, ecg=1, eog=4 Channel names: Fpz, Fp1, Fp2, AF3, AF4, AF7, AF8, Fz, F1, F2, F3, F4, F5, F6, F7, F8, FCz, FC1, FC2, FC3, FC4, FC5, FC6, FT7, FT8, Cz, C1, C2, C3, C4, C5, C6, T7, T8, CP1, CP2, CP3, CP4, CP5, CP6, TP7, TP8, Pz, P3, P4, P5, P6, P7, P8, POz, PO3, PO4, PO5, PO6, PO7, PO8, Oz, O1, O2 Montage: standard_1005 Hardware: Neuracle NeuSen W Sensor type: Ag/AgCl Line frequency: 50.0 Hz Online filters: {}

Participants

Number of subjects: 51 Health status: healthy Age: min=17.0, max=30.0 Gender distribution: female=18, male=44 Handedness: right-handed BCI experience: naive Species: human

Experimental Protocol

Paradigm: imagery Number of classes: 2 Class labels: left_hand, right_hand Trial duration: 7.5 s Study design: Multi-day MI-BCI: 2C (left/right hand, 51 subj) and 3C (left hand, right hand, foot-hooking, 11 subj). 3 sessions per subject on different days. Feedback type: none Stimulus type: video cues Stimulus modalities: visual, auditory 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

Paradigm-Specific Parameters

Detected paradigm: motor_imagery Imagery tasks: left_hand, right_hand, feet Cue duration: 1.5 s Imagery duration: 4.0 s

Data Structure

Trials: 39600 Trials context: 51 subjects x 3 sessions x 200 trials (2C) + 11 subjects x 3 sessions x 300 trials (3C) = 39600

Signal Processing

Classifiers: CSP+SVM, FBCSP+SVM, EEGNet, deepConvNet, FBCNet Feature extraction: CSP, FBCSP Frequency bands: bandpass=[0.5, 40.0] Hz Spatial filters: CSP, FBCSP

Cross-Validation

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

BCI Application

Applications: motor_control Environment: laboratory Online feedback: False

Tags

Pathology: Healthy Modality: Motor Type: Research

Documentation

DOI: 10.1038/s41597-025-04826-y License: CC-BY-4.0 Investigators: Banghua Yang, Fenqi Rong, Yunlong Xie, Du Li, Jiayang Zhang, Fu Li, Guangming Shi, Xiaorong Gao Institution: Shanghai University Country: CN Data URL: https://plus.figshare.com/articles/dataset/22671172 Publication year: 2025

References

Yang, B., Rong, F., Xie, Y., et al. (2025). A multi-day and high-quality EEG dataset for motor imagery brain-computer interface. Scientific Data, 12, 488. https://doi.org/10.1038/s41597-025-04826-y 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	`NM000348`
Title	Yang et al. 2025 — A multi-day and high-quality EEG dataset for motor imagery brain-computer interface
Author (year)	`Yang2025`
Canonical	—
Importable as	`NM000348`, `Yang2025`
Year	2025
Authors	Banghua Yang, Fenqi Rong, Yunlong Xie, Du Li, Jiayang Zhang, Fu Li, Guangming Shi, Xiaorong Gao
License	CC-BY-4.0
Citation / DOI	doi:10.1038/s41597-025-04826-y
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: 51
Recordings: 153
Tasks: 1

Channels & sampling rate

Channels: 64
Sampling rate (Hz): 1000.0
Duration (hours): 98.42606861111108

Electrode Layout#

Electrode layout — EEG · 59 sensors — 59 channels

Dataset Statistics#

Age distribution (n=51, range 29–29 yr)

25

Sex distribution

45

6

Female Male Total: 51

Channel counts: 64 ch (n=153 recordings)

Sampling frequencies: 1000.0 Hz (n=153 recordings)

Total recording duration: 98 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 NM000348 class to access this dataset programmatically.

class eegdash.dataset.NM000348(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#

Bases: EEGDashDataset

Yang et al. 2025 — A multi-day and high-quality EEG dataset for motor imagery brain-computer interface

Study:: nm000348 (NeMAR)
Author (year):: Yang2025
Canonical:: —

Also importable as: NM000348, Yang2025.

Modality: eeg; Experiment type: Motor; Subject type: Healthy. Subjects: 51; recordings: 153; 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/nm000348 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=nm000348 DOI: https://doi.org/10.1038/s41597-025-04826-y

Examples

>>> from eegdash.dataset import NM000348
>>> dataset = NM000348(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)