NM000270: eeg dataset, 27 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)
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 (—). Liu et al. 2025 — Lower limb motor imagery EEG dataset based on the multi-paradigm and longitudinal-training of stroke patients (Tianjin University). 10.1038/s41597-025-04618-4

Modality: eeg Subjects: 27 Recordings: 110 License: CC-BY-NC-ND-4.0 Source: nemar

Metadata: Complete (90%)

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 = {10.1038/s41597-025-04618-4},
  url = {https://doi.org/10.1038/s41597-025-04618-4},
}

About This Dataset#

No README content is available for this dataset.

Dataset Information#

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	—
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	doi:10.1038/s41597-025-04618-4
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: 27
Recordings: 110
Tasks: 1

Channels & sampling rate

Channels: 64
Sampling rate (Hz): 1000.0
Duration (hours): 8.997747222222223

Electrode Layout#

Electrode layout — EEG · 59 sensors — 59 channels

Dataset Statistics#

Channel counts: 64 ch (n=110 recordings)

Sampling frequencies: 1000.0 Hz (n=110 recordings)

Total recording duration: 8 h 59 min

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 NM000270 class to access this dataset programmatically.

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

Bases: EEGDashDataset

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: 27; recordings: 110; 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/nm000270 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=nm000270 DOI: https://doi.org/10.1038/s41597-025-04618-4

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)