moabb.datasets.Liu2025

class moabb.datasets.Liu2025(sessions=None, subjects=None, selected_sessions=None)

Bases: BaseDataset

[source]

Dataset Snapshot

Liu2025

Motor Imagery, 2 classes (gait_imagery vs rest)

AuthorsYuan Liu, Zhuolan Gui, De Yan, Zhuang Wang, Ruisi Gao, Ningxin Han, Junying Chen, Jialing Wu, Dong Ming

🇨🇳 Tianjin University, CN·2025

Motor Imagery Code: Liu2025 27 subjects 1 session 64 ch (60 EEG) 1000 Hz 2 classes 5.0 s trials CC BY-NC-ND 4.0

Class Labels: gait_imagery, rest

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Overview

Lower limb motor imagery dataset from Liu et al 2025.

Dataset from Lower limb motor imagery EEG dataset based on the multi-paradigm and longitudinal-training of stroke patients

It contains EEG from 27 stroke patients recorded with a 64-channel NeuSen W (Neuracle) system at 1000 Hz. The task is binary: gait motor imagery vs idle state.

Five experiment types (mapped to BIDS sessions):

• - ses-pre: Pre-treatment conventional MI (all 27 subjects)
• ses-mises: MI with sequential electrical stimulation (16 subj)
• ses-miies: MI with invariable electrical stimulation (16 subj)
• ses-post: Post-treatment assessment (all 27 subjects)
• ses-follow: Follow-up assessment (all 27 subjects)

Each session has ~4 runs of 10 MI + 10 idle trials (20 per run). Trial structure: 3 s baseline, 1 s cue, 5 s MI/idle, rest.

Event codes: 3 = gait MI onset (gait_imagery), 9 = idle onset (rest). Only these two events are used for classification; codes 1-2 and 7-8 are baseline/instruction markers.

:param sessions: Which sessions to load. One or more of "pre", "mises", "miies", "post", "follow". Default: "pre" only. :type sessions: str or list of str, optional

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Citation & Impact

• Paper DOI10.1038/s41597-025-04618-4
• CitationsLoading…
• Public APICrossref | OpenAlex

Stimulus Protocol

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

HED Event Tags

HED tags2/2 events annotated

Source: MOABB BIDS HED annotation mapping.

Sensory-event

2

Experimental-stimulus

1

Label

1

Rest

1

Visual-presentation

1

rest

Sensory-eventExperimental-stimulusVisual-presentationRest

gait_imagery

Sensory-eventLabel

HED tree view

Tree · rest

├─ Sensory-event
├─ Experimental-stimulus
├─ Visual-presentation
└─ Rest

Tree · gait_imagery

├─ Sensory-event
└─ Label

Channel Summary

Total channels64

EEG60 (Ag/AgCl)

EOG4

ECG1

Montagestandard_1020

Sampling1000 Hz

Filter{'notch': 50.0}

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 motor imagery dataset from Liu et al 2025.

Dataset from Lower limb motor imagery EEG dataset based on the multi-paradigm and longitudinal-training of stroke patients [1].

It contains EEG from 27 stroke patients recorded with a 64-channel NeuSen W (Neuracle) system at 1000 Hz. The task is binary: gait motor imagery vs idle state.

Five experiment types (mapped to BIDS sessions):

• ses-pre: Pre-treatment conventional MI (all 27 subjects)

• ses-mises: MI with sequential electrical stimulation (16 subj)

• ses-miies: MI with invariable electrical stimulation (16 subj)

• ses-post: Post-treatment assessment (all 27 subjects)

• ses-follow: Follow-up assessment (all 27 subjects)

Each session has ~4 runs of 10 MI + 10 idle trials (20 per run). Trial structure: 3 s baseline, 1 s cue, 5 s MI/idle, rest.

Event codes: 3 = gait MI onset (gait_imagery), 9 = idle onset (rest). Only these two events are used for classification; codes 1-2 and 7-8 are baseline/instruction markers.

param sessions:

Which sessions to load. One or more of "pre", "mises", "miies", "post", "follow". Default: "pre" only.

type sessions:

str or list of str, optional

References

[1]

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

Code Examples

from moabb.datasets import Liu2025
dataset = Liu2025()
data = dataset.get_data(subjects=[1])
print(data[1])

Metadata

Dataset summary

#Subj	27
#Chan	64
#Classes	2
#Trials / class	40
Trials length	5 s
Freq	1000 Hz
#Sessions	3
#Runs	4
Total_trials	8640

Participants

• Population: stroke (recovery phase)

• Clinical population: stroke

• Handedness: mixed

Equipment

• Amplifier: NeuSen W (Neuracle, Inc.)

• Electrodes: Ag/AgCl

• Montage: standard_1020

Data Access

• DOI: 10.1038/s41597-025-04618-4

• Data URL: https://zenodo.org/records/18987384

Experimental Protocol

• Paradigm: imagery

• Feedback: none

• Stimulus: visual cue

__init__(sessions=None, subjects=None, selected_sessions=None)

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)

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

CacheConfig

Cache configuration for get_data().

moabb.datasets.bids_interface.get_bids_root

Return the BIDS root path.

Notes

Added in version 1.5.

data_path(subject, path=None, force_update=False, update_path=None, verbose=None)

Get path to local copy of a subject data.

Parameters:

• subject (int) – Number of subject to use

• path (None | str) – Location of where to look for the data storing location. If None, the environment variable or config parameter MNE_DATASETS_(dataset)_PATH is used. If it doesn’t exist, the “~/mne_data” directory is used. If the dataset is not found under the given path, the data will be automatically downloaded to the specified folder.

• force_update (bool) – Force update of the dataset even if a local copy exists.

• update_path (bool | None Deprecated) – If True, set the MNE_DATASETS_(dataset)_PATH in mne-python config to the given path. If None, the user is prompted.

• verbose (bool, str, int, or None) – If not None, override default verbose level (see mne.verbose()).

Returns:

path – Local path to the given data file. This path is contained inside a list of length one, for compatibility.

Return type:

list of str

download(subject_list=None, path=None, force_update=False, update_path=None, accept=False, verbose=None)

Download all data from the dataset.

This function is only useful to download all the dataset at once.

Parameters:

• subject_list (list of int | None) – List of subjects id to download, if None all subjects are downloaded.

• path (None | str) – Location of where to look for the data storing location. If None, the environment variable or config parameter MNE_DATASETS_(dataset)_PATH is used. If it doesn’t exist, the “~/mne_data” directory is used. If the dataset is not found under the given path, the data will be automatically downloaded to the specified folder.

• force_update (bool) – Force update of the dataset even if a local copy exists.

• update_path (bool | None) – If True, set the MNE_DATASETS_(dataset)_PATH in mne-python config to the given path. If None, the user is prompted.

• accept (bool) – Accept licence term to download the data, if any. Default: False

• verbose (bool, str, int, or None) – If not None, override default verbose level (see mne.verbose()).

get_additional_metadata(subject: str, session: str, run: str) → None | DataFrame

Load additional metadata for a specific subject, session, and run.

This method is intended to be overridden by subclasses to provide additional metadata specific to the dataset. The metadata is typically loaded from an events.tsv file or similar data source.

Parameters:

• subject (str) – The identifier for the subject.

• session (str) – The identifier for the session.

• run (str) – The identifier for the run.

Returns:

A DataFrame containing the additional metadata if available, otherwise None.

Return type:

None | pd.DataFrame

get_block_repetition(paradigm, subjects, block_list, repetition_list)

Select data for all provided subjects, blocks and repetitions.

subject -> session -> run -> block -> repetition

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)

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

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