NM000246: eeg dataset, 51 subjects#

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

Access recordings and metadata through EEGDash.

Citation: Banghua Yang, Fenqi Rong, Yunlong Xie, Du Li, Jiayang Zhang, Fu Li, Guangming Shi, Xiaorong Gao (2025). Multi-day MI-BCI dataset (WBCIC-SHU) from Yang et al 2025.

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

Metadata: Complete (90%)

Quickstart#

Get Started

Install

pip install eegdash

Access the data

from eegdash.dataset import NM000246

dataset = NM000246(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 = NM000246(cache_dir="./data", subject="01")

Advanced query

dataset = NM000246(
    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{nm000246,
  title = {Multi-day MI-BCI dataset (WBCIC-SHU) from Yang et al 2025},
  author = {Banghua Yang and Fenqi Rong and Yunlong Xie and Du Li and Jiayang Zhang and Fu Li and Guangming Shi and Xiaorong Gao},
}

About This Dataset#

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

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

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

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	`NM000246`
Title	Multi-day MI-BCI dataset (WBCIC-SHU) from Yang et al 2025
Author (year)	`Yang2025_Multi`
Canonical	—
Importable as	`NM000246`, `Yang2025_Multi`
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	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: 51
Recordings: 153
Tasks: 1

Channels & sampling rate

Channels: 59
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: 59 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 NM000246 class to access this dataset programmatically.

class eegdash.dataset.NM000246(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#

Bases: EEGDashDataset

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

Study:: nm000246 (NeMAR)
Author (year):: Yang2025_Multi
Canonical:: —

Also importable as: NM000246, Yang2025_Multi.

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/nm000246 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=nm000246

Examples

>>> from eegdash.dataset import NM000246
>>> dataset = NM000246(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)