moabb.datasets.Zuo2025#

class moabb.datasets.Zuo2025(subjects=None, sessions=None, *, return_all_modalities=False)[source]#

Bases: BaseDataset

Dataset Snapshot

Zuo2025

Motor Imagery, 2 classes (left_leg vs right_leg)

AuthorsChongwen Zuo, Yi Yin, Haochong Wang, Zhiyang Zheng, Xiaoyan Ma, Yuan Yang, Jue Wang, Shan Wang, Zi-gang Huang, Chaoqun Ye

🇨🇳 Air Force Medical Center, Beijing, CN·2025

Motor Imagery Code: Zuo2025 30 subjects 5 sessions 30 ch 500 Hz 2 classes 4.0 s trials CC BY 4.0

Class Labels: left_leg, right_leg

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Overview

Lower-limb MI dataset for knee pain patients from Zuo et al. 2025.

Dataset from

This dataset contains 30-channel EEG recordings from 30 knee pain patients performing left and right leg motor imagery. Recorded with ZhenTec EEG system at 500 Hz using the 10-20 montage.

Each subject completed 5 sessions (every 2 days), with 100 trials per session (50 left leg, 50 right leg). Trial structure: 4 s MI, 4 s rest, ~2 s gap.

Open in Overview tab →

Citation & Impact

Paper DOI10.1038/s41597-025-05767-2
CitationsLoading…
Public APICrossref | OpenAlex

Stimulus Protocol

4s task window per trial · 2-class motor imagery paradigm · 1 runs/session across 5 sessions

HED Event Tags

HED tags2/2 events annotated

Source: MOABB BIDS HED annotation mapping.

Label

2

Sensory-event

2

left_leg

Sensory-eventLabel

right_leg

Sensory-eventLabel

HED tree view

Tree · left_leg

├─ Sensory-event
└─ Label

Tree · right_leg

├─ Sensory-event
└─ Label

Channel Summary

Total channels30

EEG30

Montage10-05

Sampling500 Hz

ReferenceCPz

Notch / line50 Hz

This diagram is automatically generated from MOABB metadata. Please consult the original publication to confirm the experimental protocol details.

Overview

Lower-limb MI dataset for knee pain patients from Zuo et al. 2025.

Dataset from [1].

This dataset contains 30-channel EEG recordings from 30 knee pain patients performing left and right leg motor imagery. Recorded with ZhenTec EEG system at 500 Hz using the 10-20 montage.

Each subject completed 5 sessions (every 2 days), with 100 trials per session (50 left leg, 50 right leg). Trial structure: 4 s MI, 4 s rest, ~2 s gap.

Note

12 of 30 subjects have incomplete raw session files on Figshare (32 of 150 sessions missing). The adapter loads all available sessions per subject. Each session file is ~100 MB.

Note

There is a known inconsistency between the dataset README and events.json regarding the left/right leg label mapping. This adapter follows the events.json mapping (generated programmatically).

References

[1]

Zuo, C., Yin, Y., Wang, H., et al. (2025). Enhancing classification of a large lower-limb motor imagery EEG dataset for BCI in knee pain patients. Scientific Data, 12, 1451. https://doi.org/10.1038/s41597-025-05767-2

Code Examples

from moabb.datasets import Zuo2025
dataset = Zuo2025()
data = dataset.get_data(subjects=[1])
print(data[1])

Metadata

Dataset summary

#Subj	30
#Chan	30
#Classes	2
#Trials / class	50
Trials length	4 s
Freq	500 Hz
#Sessions	5
#Runs	1
Total_trials	15000

Participants

Population: knee pain patients
Clinical population: knee_pain
Age: 33.5 (range: 24-45) years

Equipment

Amplifier: ZhenTec EEG system
Montage: standard_1005
Reference: CPz

Data Access

DOI: 10.1038/s41597-025-05767-2
Data URL: https://figshare.com/articles/dataset/28740260

Experimental Protocol

Paradigm: imagery
Feedback: none
Stimulus: visual

Notes

Added in version 1.2.0.

__init__(subjects=None, sessions=None, *, return_all_modalities=False)[source]#: Initialize function for the BaseDataset.

property all_subjects#: Full list of subjects available in this dataset (unfiltered).

convert_to_bids(path=None, subjects=None, overwrite=False, format='EDF', verbose=None)[source]#

Convert the dataset to BIDS format.

Saves the raw EEG data in a BIDS-compliant directory structure. Unlike the caching mechanism (see CacheConfig), the files produced here do not contain a processing-pipeline hash (desc-<hash>) in their names, making the output a clean, shareable BIDS dataset.

Parameters:

path (str | Path | None) – Directory under which the BIDS dataset will be written. If None the default MNE data directory is used (same default as the rest of MOABB).
subjects (list of int | None) – Subject numbers to convert. If None, all subjects in subject_list are converted.
overwrite (bool) – If True, existing BIDS files for a subject are removed before saving. Default is False.
format (str) – The file format for the raw EEG data. Supported values are "EDF" (default), "BrainVision", and "EEGLAB".
verbose (str | None) – Verbosity level forwarded to MNE/MNE-BIDS.

Returns:

bids_root – Path to the root of the written BIDS dataset.

Return type:

pathlib.Path

Examples

>>> from moabb.datasets import AlexMI
>>> dataset = AlexMI()
>>> bids_root = dataset.convert_to_bids(path='/tmp/bids', subjects=[1])

See also

BaseDataset.get_data

Parameters:

subjects (List of int) – List of subject number
block_list (List of int) – List of block number
repetition_list (List of int) – List of repetition number inside a block

Returns:

data – dict containing the raw data

Return type:

Dict

get_data(subjects=None, cache_config=None, process_pipeline=None)[source]#

Return the data corresponding to a list of subjects.

The returned data is a dictionary with the following structure:

data = {'subject_id' :
            {'session_id':
                {'run_id': run}
            }
        }

subjects are on top, then we have sessions, then runs. A sessions is a recording done in a single day, without removing the EEG cap. A session is constitued of at least one run. A run is a single contiguous recording. Some dataset break session in multiple runs.

Processing steps can optionally be applied to the data using the *_pipeline arguments. These pipelines are applied in the following order: raw_pipeline -> epochs_pipeline -> array_pipeline. If a *_pipeline argument is None, the step will be skipped. Therefore, the array_pipeline may either receive a mne.io.Raw or a mne.Epochs object as input depending on whether epochs_pipeline is None or not.

Parameters:

subjects (List of int) – List of subject number
cache_config (dict | CacheConfig) – Configuration for caching of datasets. See CacheConfig for details.
process_pipeline (Pipeline | None) – Optional processing pipeline to apply to the data. To generate an adequate pipeline, we recommend using moabb.utils.make_process_pipelines(). This pipeline will receive mne.io.BaseRaw objects. The steps names of this pipeline should be elements of StepType. According to their name, the steps should either return a mne.io.BaseRaw, a mne.Epochs, or a numpy.ndarray(). This pipeline must be “fixed” because it will not be trained, i.e. no call to fit will be made.

Returns:

data – dict containing the raw data

Return type:

Dict

property metadata: DatasetMetadata | None[source]#

Return structured metadata for this dataset.

Returns the DatasetMetadata object from the centralized catalog, or None if metadata is not available for this dataset.

Returns:: The metadata object containing acquisition parameters, participant demographics, experiment details, and documentation. Returns None if no metadata is registered for this dataset.
Return type:: DatasetMetadata | None

Examples

>>> from moabb.datasets import BNCI2014_001
>>> dataset = BNCI2014_001()
>>> dataset.metadata.participants.n_subjects
9
>>> dataset.metadata.acquisition.sampling_rate
250.0