EEGdash›NeMAR›NM000160
Iss. 160 · 18 subjects · 141 recordings · CC-BY-NC-ND-4.0
Dataset Brief · Multi-joint upper-limb MI dataset from Yi et al. 2025

NM000160: eeg dataset, 18 subjects#

Multi-joint upper-limb MI dataset from Yi et al. 2025

Citation: Weibo Yi, Jiaming Chen, Dan Wang, Xinkang Hu, Meng Xu, Fangda Li, Shuhan Wu, Jin Qian (2025). Multi-joint upper-limb MI dataset from Yi et al. 2025. 10.82901/nemar.nm000160

18-participant EEG dataset — Multi-joint upper-limb MI dataset from Yi et al. 2025.

Data & curation Weibo Yi · Jiaming Chen · Dan Wang · Xinkang Hu · Meng Xu · Fangda Li · …
Year 2025 · Distributed via NeMAR
EEG · 62 ch1000 HzBIDS 1.9.0Task · imageryHealthyVisualMotor
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#

Install

pip install eegdash

Access the data

from eegdash.dataset import NM000160

dataset = NM000160(cache_dir="./data")
# Get the raw object of the first recording
raw = dataset.datasets[0].raw
print(raw.info)

Filter by subject

dataset = NM000160(cache_dir="./data", subject="01")

Advanced query

dataset = NM000160(
    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{nm000160,
  title = {Multi-joint upper-limb MI dataset from Yi et al. 2025},
  author = {Weibo Yi and Jiaming Chen and Dan Wang and Xinkang Hu and Meng Xu and Fangda Li and Shuhan Wu and Jin Qian},
  doi = {10.82901/nemar.nm000160},
  url = {https://doi.org/10.82901/nemar.nm000160},
}
§ 02Study · The README

About This Dataset#

Multi-joint upper-limb MI dataset from Yi et al. 2025.

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

DOI

Multi-joint upper-limb MI dataset from Yi et al. 2025

hand_open_close
View full README

DOI

Multi-joint upper-limb MI dataset from Yi et al. 2025

hand_open_close

     ├─ Sensory-event
     └─ Label/hand_open_close

wrist_flex_ext

     ├─ Sensory-event
     └─ Label/wrist_flex_ext

wrist_abd_add

     ├─ Sensory-event
     └─ Label/wrist_abd_add

elbow_pron_sup

     ├─ Sensory-event
     └─ Label/elbow_pron_sup

elbow_flex_ext

     ├─ Sensory-event
     └─ Label/elbow_flex_ext

shoulder_pron_sup

     ├─ Sensory-event
     └─ Label/shoulder_pron_sup

shoulder_abd_add

     ├─ Sensory-event
     └─ Label/shoulder_abd_add

shoulder_flex_ext

├─ Sensory-event
└─ Label/shoulder_flex_ext

Paradigm-Specific Parameters

  • Detected paradigm: motor_imagery

  • Imagery tasks: hand_open_close, wrist_flex_ext, wrist_abd_add, elbow_pron_sup, elbow_flex_ext, shoulder_pron_sup, shoulder_abd_add, shoulder_flex_ext

  • Cue duration: 2.0 s

  • Imagery duration: 4.0 s

Data Structure

  • Trials: 320

  • Trials per class: hand_open_close=40, wrist_flex_ext=40, wrist_abd_add=40, elbow_pron_sup=40, elbow_flex_ext=40, shoulder_pron_sup=40, shoulder_abd_add=40, shoulder_flex_ext=40

  • Blocks per session: 8

  • Trials context: 8 blocks x 40 trials (5 per class x 8 classes)

Signal Processing

  • Classifiers: ShallowConvNet

  • Feature extraction: ERSP

  • Frequency bands: alpha=[8.0, 13.0] Hz; beta=[13.0, 30.0] Hz; bandpass=[4.0, 40.0] Hz

  • Spatial filters: CAR

Cross-Validation

  • Method: 5-fold

  • Folds: 5

  • Evaluation type: within_subject

BCI Application

  • Applications: rehabilitation

  • Environment: laboratory

  • Online feedback: False

Tags

  • Pathology: Healthy

  • Modality: Motor

  • Type: Motor Imagery

Documentation

  • DOI: 10.1038/s41597-025-05286-0

  • License: CC-BY-NC-ND-4.0

  • Investigators: Weibo Yi, Jiaming Chen, Dan Wang, Xinkang Hu, Meng Xu, Fangda Li, Shuhan Wu, Jin Qian

  • Institution: Beijing University of Technology

  • Country: CN

  • Data URL: https://figshare.com/articles/dataset/Data/24123303

  • Publication year: 2025

References

Yi, W., Chen, J., Wang, D., et al. (2025). A multi-modal dataset of EEG and fNIRS for motor imagery of multi-types of joints from unilateral upper limb. Scientific Data, 12, 953. https://doi.org/10.1038/s41597-025-05286-0 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

§ 03Cohort · Participants

Cohort#

Dataset Statistics#

Channel counts: 62 ch (n=141 recordings)

Sampling frequencies: 1000.0 Hz (n=141 recordings)

Total recording duration: 32 h

§ 04Signal · Electrodes & trace

Signal · Electrodes & live trace#

Fig. 01 Signal & montage 62 ch · EEG · 1000 Hz · 18 subjects, 141 recordings
Live trace viewer — sub-13 · ses-0 · task-imagery · run-4

Showing one representative recording out of 18 subjects and 141 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 · 62 sensors — 62 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.

NEMAR pipeline statistics — NM000160
HED event descriptors word cloud HED event descriptors word cloud — NM000160
§ 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…
Full dataset metadata table

Dataset ID

NM000160

Title

Multi-joint upper-limb MI dataset from Yi et al. 2025

Author (year)

Yi2025

Canonical

—

Importable as

NM000160, Yi2025

Year

2025

Authors

Weibo Yi, Jiaming Chen, Dan Wang, Xinkang Hu, Meng Xu, Fangda Li, Shuhan Wu, Jin Qian

License

CC-BY-NC-ND-4.0

Citation / DOI

10.82901/nemar.nm000160

Source links

OpenNeuro | NeMAR | Source URL
Copy-paste BibTeX
@dataset{nm000160,
  title = {Multi-joint upper-limb MI dataset from Yi et al. 2025},
  author = {Weibo Yi and Jiaming Chen and Dan Wang and Xinkang Hu and Meng Xu and Fangda Li and Shuhan Wu and Jin Qian},
  doi = {10.82901/nemar.nm000160},
  url = {https://doi.org/10.82901/nemar.nm000160},
}
§ 06API · Programmatic access

API Reference#

Signature
eegdash.dataset
class
eegdash.dataset.NM000160(cache_dir, query=None, s3_bucket=None, **kwargs)
Bases: EEGDashDataset
Author (year)Yi2025
Canonical—
Importable asNM000160 · Yi2025
Sourceeegdash/dataset/registry.py · [source ↗]
class eegdash.dataset.NM000160(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#

Multi-joint upper-limb MI dataset from Yi et al. 2025

Study:

nm000160 (NeMAR)

Author (year):

Yi2025

Canonical:

—

Also importable as: NM000160, Yi2025.

Modality: eeg; Experiment type: Motor; Subject type: Healthy. Subjects: 18; recordings: 141; 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/nm000160 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=nm000160 DOI: https://doi.org/10.82901/nemar.nm000160

Examples

>>> from eegdash.dataset import NM000160
>>> dataset = NM000160(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 — NM000160.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 nm000160 to reproduce the tutorial on this dataset.

Citation

Weibo Yi, Jiaming Chen, Dan Wang, Xinkang Hu, Meng Xu, … (2025). Multi-joint upper-limb MI dataset from Yi et al. 2025. 10.82901/nemar.nm000160

Provenance

¹Contributed to nemar in BIDS format.

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

³Persistent identifier: 10.82901/nemar.nm000160.

BIDS
BIDS 1.9.0
Sidecars
events · events.json · channels · eeg.json
Provenance
CC-BY-NC-ND-4.0 · 10.82901/nemar.nm000160
Machine-readable
schema.org/Dataset · Croissant
Mirrors
OpenNeuro · NEMAR · HF org

See Also#