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#

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)

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'])

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) https://github.com/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

WBCIC_SHU, WBCICSHU

Importable as

NM000246, Yang2025_Multi, WBCIC_SHU, WBCICSHU

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

Tags

  • Pathology: Healthy

  • Modality: Visual

  • Type: Motor

Files & format

  • Size on disk: 58.4 GB

  • File count: 153

  • Format: BIDS

License & citation

  • License: CC-BY-4.0

  • DOI: —

Provenance

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:

WBCIC_SHU, WBCICSHU

Also importable as: NM000246, Yang2025_Multi, WBCIC_SHU, WBCICSHU.

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, overwrite=False)[source]#

Save the dataset to disk.

Parameters:

  • path (str or Path) – Destination file path.

  • overwrite (bool, default False) – If True, overwrite existing file.

Return type:

None

See Also#