EEGdash›NeMAR›NM000348

Iss. 348 · 51 subjects · 153 recordings · CC-BY-4.0

Dataset Brief · Yang et al. 2025 — A multi-day and high-quality EEG dataset f…

NM000348: eeg dataset, 51 subjects#

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

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

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

Data & curation Banghua Yang · Fenqi Rong · Yunlong Xie · Du Li · Jiayang Zhang · Fu Li · …
Year 2025 · Distributed via NeMAR

EEG · 64 ch1000 HzBIDS 1.9.0Task · imagery3 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 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},
}

§ 02Study · The README

About This Dataset#

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

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

Yang2025

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
View full README
Yang2025

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

§ 03Cohort · Participants

Cohort#

Dataset Statistics#

Age distribution by gender (n=51, range 29–29 yr, mean 29.0 yr)

25

Female · 45Male · 6

Sex composition

Female
45
Male
6
F : M ratio
7.50 : 1

88% female · n = 51 subjects with reported sex.

HandednessRight · 51

Channel counts: 64 ch (n=153 recordings)

Sampling frequencies: 1000.0 Hz (n=153 recordings)

Total recording duration: 98 h

§ 04Signal · Electrodes & trace

Signal · Electrodes & live trace#

Fig. 01 Signal & montage 64 ch · EEG · 1000 Hz · 51 subjects, 153 recordings

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

Showing one representative recording out of 51 subjects and 153 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 · 59 sensors — 59 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.NM000348(cache_dir, query=None, s3_bucket=None, **kwargs)

Bases: EEGDashDataset

Author (year)Yang2025

Canonical—

Importable asNM000348 · Yang2025

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

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

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)

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 — NM000348.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 nm000348 to reproduce the tutorial on this dataset.

Citation

Banghua Yang, Fenqi Rong, Yunlong Xie, Du Li, Jiayang Zhang, … (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

Provenance

¹Contributed to nemar in BIDS format.

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

³Persistent identifier: 10.1038/s41597-025-04826-y.

Related & sibling datasets

NM000246EEG · 51 subjSame authors NM000338EEG · 54 subj NM000265EEG · 31 subj NM000209EEG · 25 subj NM000227EEG · 31 subj

+ 1 more — see See Also below →

BIDS

BIDS 1.9.0

Sidecars

events · events.json · channels · eeg.json

Provenance

CC-BY-4.0 · 10.1038/s41597-025-04826-y

Machine-readable

schema.org/Dataset · Croissant

Mirrors

OpenNeuro · NEMAR · HF org

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

NM000348: eeg dataset, 51 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#