EEGdash›NeMAR›NM000270

Iss. 270 · 36 subjects · 529 recordings · CC-BY-NC-ND-4.0

Dataset Brief · Liu et al. 2025 — Lower limb motor imagery EEG dataset based…

NM000270: eeg dataset, 36 subjects#

Name: Liu et al. 2025 — Lower limb motor imagery EEG dataset based on the multi-paradigm and longitudinal-training of stroke patients (Tianjin University)
Published: 2025-01-01
License: CC-BY-NC-ND-4.0

Liu et al. 2025 — Lower limb motor imagery EEG dataset based on the multi-paradigm and longitudinal-training of stroke patients (Tianjin University)

Access recordings and metadata through EEGDash.

Citation: Yuan Liu, Zhuolan Gui, De Yan, Zhuang Wang, Ruisi Gao, Ningxin Han, Junying Chen, Jialing Wu, Dong Ming (2025). Liu et al. 2025 — Lower limb motor imagery EEG dataset based on the multi-paradigm and longitudinal-training of stroke patients (Tianjin University). 10.82901/nemar.nm000270

Modality: eeg Subjects: 36 Recordings: 529 License: CC-BY-NC-ND-4.0 Source: nemar

Metadata: Complete (100%)

36-participant EEG dataset — Liu et al. 2025 — Lower limb motor imagery EEG dataset based on the multi-paradigm and longitudinal-training of stroke patients (Tianjin University).

Data & curation Yuan Liu · Zhuolan Gui · De Yan · Zhuang Wang · Ruisi Gao · Ningxin Han · …
Senior author Dong Ming
Year 2025 · Distributed via NeMAR

EEG · 64 ch1000 HzBIDS 1.9.02 tasks6 sessionsMotor

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 NM000270

dataset = NM000270(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 = NM000270(cache_dir="./data", subject="01")

Advanced query

dataset = NM000270(
    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{nm000270,
  title = {Liu et al. 2025 — Lower limb motor imagery EEG dataset based on the multi-paradigm and longitudinal-training of stroke patients (Tianjin University)},
  author = {Yuan Liu and Zhuolan Gui and De Yan and Zhuang Wang and Ruisi Gao and Ningxin Han and Junying Chen and Jialing Wu and Dong Ming},
  doi = {10.82901/nemar.nm000270},
  url = {https://doi.org/10.82901/nemar.nm000270},
}

§ 02Study · The README

About This Dataset#

This dataset contains scalp electroencephalography (EEG) recordings acquired during a

lower-limb motor-imagery**paradigm in ** stroke patients undergoing rehabilitation.

It was collected with a multi-paradigm protocol and a longitudinal (repeated-session)

training design, supporting research on motor-imagery brain–computer interfaces (BCI) for lower-limb and gait rehabilitation after stroke.

DOI

Lower limb motor imagery EEG dataset of stroke patients (multi-paradigm, longitudinal training)

Summary

The BIDS conversion in this NEMAR record contains EEG data for 27 participants (sub-* folders), organised under the eeg/ modality with a single motor-imagery task (task-imagery).

NEMAR Metadata#

[![DOI](https://img.shields.io/badge/DOI-10.82901%2Fnemar.nm000270-blue)](https://doi.org/10.82901/nemar.nm000270) # Lower limb motor imagery EEG dataset of stroke patients (multi-paradigm, longitudinal training) ## Summary This dataset contains scalp electroencephalography (EEG) recordings acquired during a lower-limb motor-imagery paradigm in stroke patients undergoing rehabilitation. It was collected with a multi-paradigm protocol and a longitudinal (repeated-session) training design, supporting research on motor-imagery brain–computer interfaces (BCI) for lower-limb and gait rehabilitation after stroke. The BIDS conversion in this NEMAR record contains EEG data for 27 participants (sub-* folders), organised under the eeg/ modality with a single motor-imagery task (task-imagery). ## Modality and paradigm - Modality: EEG (scalp electroencephalography) - Task / paradigm: Lower-limb motor imagery (task-imagery), multi-paradigm,

longitudinal training

Population: Stroke patients (rehabilitation cohort)

## Participants This BIDS dataset includes 27 subjects (sub-01 … ). Refer to participants.tsv and the original data descriptor for demographic and clinical details. ## Original dataset / data paper Please cite the original Scientific Data descriptor when using this dataset: > Liu, Y., Gui, Z., Yan, D., Wang, Z., Gao, R., Han, N., Chen, J., Wu, J., & Ming, D. > (2025). Lower limb motor imagery EEG dataset based on the multi-paradigm and > longitudinal-training of stroke patients. Scientific Data, 12, 314. > https://doi.org/10.1038/s41597-025-04618-4 - DOI: [10.1038/s41597-025-04618-4](https://doi.org/10.1038/s41597-025-04618-4) - Source / project: Tianjin University - Related record: https://zenodo.org/records/18987384 ## Attribution All data were collected by the original authors (Yuan Liu and colleagues, Tianjin University). Please credit the original creators and cite the data paper above. This NEMAR record redistributes the dataset in BIDS format; EEG-BIDS and related BIDS tools were used only for standardisation, not as the source of the data. ## License CC-BY-NC-ND-4.0 (see dataset_description.json).

License: CC-BY-NC-ND-4.0

Authors:

Yuan Liu
Zhuolan Gui
De Yan
Zhuang Wang
Ruisi Gao
… and 4 more

Versions:

Version	DOI	Released
`current`	10.82901/nemar.nm000270

§ 03Cohort · Participants

Cohort#

Dataset Statistics#

Channel counts: 64 ch (n=529 recordings)

Sampling frequencies: 1000.0 Hz (n=529 recordings)

Total recording duration: 42 h

§ 04Signal · Electrodes & trace

Signal · Electrodes & live trace#

Fig. 01 Signal & montage 64 ch · EEG · 1000 Hz · 36 subjects, 529 recordings

Live trace viewer — sub-01 · ses-follow · task-MotorImagery · run-01

Showing one representative recording out of 36 subjects and 529 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.NM000270(cache_dir, query=None, s3_bucket=None, **kwargs)

Bases: EEGDashDataset

Author (year)Liu2025

Canonical—

Importable asNM000270 · Liu2025

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

class eegdash.dataset.NM000270(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#

Liu et al. 2025 — Lower limb motor imagery EEG dataset based on the multi-paradigm and longitudinal-training of stroke patients (Tianjin University)

Study:: nm000270 (NeMAR)
Author (year):: Liu2025
Canonical:: —

Also importable as: NM000270, Liu2025.

Modality: eeg; Experiment type: Motor; Subject type: Unknown. Subjects: 36; recordings: 529; tasks: 2.

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/nm000270 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=nm000270 DOI: https://doi.org/10.82901/nemar.nm000270

Examples

>>> from eegdash.dataset import NM000270
>>> dataset = NM000270(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 FacePre-bundled mirror at EEGDash/nm000270 · pull with datasets.load_dataset("EEGDash/nm000270").huggingface

Croissant 1.0Machine-readable JSON-LD descriptor — NM000270.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 nm000270 to reproduce the tutorial on this dataset.

Citation

Yuan Liu, Zhuolan Gui, De Yan, Zhuang Wang, Ruisi Gao, … (2025). Liu et al. 2025 — Lower limb motor imagery EEG dataset based on the multi-paradigm and longitudinal-training of stroke patients (Tianjin University). 10.82901/nemar.nm000270

Provenance

¹Contributed to nemar in BIDS format.

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

³Persistent identifier: 10.82901/nemar.nm000270.

Related & sibling datasets

NM000271EEG · 28 subj ON004572EEG · 52 subj ON004444EEG · 30 subj ON007176EEG · 45 subj ON005289EEG · 39 subj

+ 1 more — see See Also below →

BIDS

BIDS 1.9.0

Sidecars

events · events.json · channels · eeg.json

Provenance

CC-BY-NC-ND-4.0 · 10.82901/nemar.nm000270

Machine-readable

schema.org/Dataset · Croissant

Mirrors

OpenNeuro · NEMAR · HuggingFace

Dataset ID	`NM000270`
Title	Liu et al. 2025 — Lower limb motor imagery EEG dataset based on the multi-paradigm and longitudinal-training of stroke patients (Tianjin University)
Author (year)	`Liu2025`
Canonical	—
Importable as	`NM000270`, `Liu2025`
Year	2025
Authors	Yuan Liu, Zhuolan Gui, De Yan, Zhuang Wang, Ruisi Gao, Ningxin Han, Junying Chen, Jialing Wu, Dong Ming
License	CC-BY-NC-ND-4.0
Citation / DOI	10.82901/nemar.nm000270
Source links	OpenNeuro \| NeMAR \| Source URL

NM000270: eeg dataset, 36 subjects#

Quickstart#

About This Dataset#

NEMAR Metadata#

Cohort#

Dataset Statistics#

Signal · Electrodes & live trace#

NEMAR Processing Statistics#

Manifest#

File Explorer#

API Reference#

Citation

Provenance

Related & sibling datasets

See Also#