moabb.datasets.Gao2026

class moabb.datasets.Gao2026(subjects=None, sessions=None)

Bases: BaseDataset

[source]

Dataset Snapshot

Gao2026

Motor Imagery, 10 classes

AuthorsJing'ao Gao, Yao Liu, Zhengshuang Li, Kaixin Huang, Fan Wang, Jiaping Xu, Lei Zhao, Tianwen Li, Yunfa Fu

🇨🇳 Kunming University of Science and Technology, CN·2026

Motor Imagery Code: Gao2026 22 subjects 2 sessions 32 ch 1000 Hz 10 classes 4.0 s trials CC BY-NC-ND 4.0

Class Labels: dog, bird, fish, pentagram, square, circle, scissor, watch, ...

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Overview

Visual imagery EEG dataset from Gao et al 2026.

Dataset from the article An EEG Dataset for Visual Imagery-Based Brain-Computer Interface

Note: This is a visual imagery dataset (not motor imagery). Participants imagined visual stimuli across three categories: animals, geometric figures, and everyday objects.

EEG was recorded from 22 healthy participants using a 32-channel Neuracle NeuSenW32 wireless cap at 1000 Hz, with Cpz reference.

Three task categories were recorded per session:

• - Animals (AVI): dog, bird, fish (3 classes, 40 trials each)
• Figures (FVI): pentagram, square, circle (3 classes, 40 trials each)
• Objects (OVI): scissor, watch, cup, chair (4 classes, 40 trials each)

Each trial: 3 s fixation, 4 s image presentation, 4 s imagery period (the epoch of interest), then rest. Events mark imagery onset.

Most subjects completed 2 sessions. Subjects 9 and 10 completed only session 1.

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Citation & Impact

• Paper DOI10.1038/s41597-025-06512-5
• CitationsLoading…
• Public APICrossref | OpenAlex

Stimulus Protocol

4s task window per trial · 10-class motor imagery paradigm · 3 runs/session across 2 sessions

HED Event Tags

HED tags10/10 events annotated

Source: MOABB BIDS HED annotation mapping.

Label

10

Sensory-event

10

dog

Sensory-eventLabel

bird

Sensory-eventLabel

fish

Sensory-eventLabel

pentagram

Sensory-eventLabel

square

Sensory-eventLabel

circle

Sensory-eventLabel

scissor

Sensory-eventLabel

watch

Sensory-eventLabel

cup

Sensory-eventLabel

chair

Sensory-eventLabel

HED tree view

Tree · dog

├─ Sensory-event
└─ Label

Tree · bird

├─ Sensory-event
└─ Label

Tree · fish

├─ Sensory-event
└─ Label

Tree · pentagram

├─ Sensory-event
└─ Label

Tree · square

├─ Sensory-event
└─ Label

Tree · circle

├─ Sensory-event
└─ Label

Tree · scissor

├─ Sensory-event
└─ Label

Tree · watch

├─ Sensory-event
└─ Label

Tree · cup

├─ Sensory-event
└─ Label

Tree · chair

├─ Sensory-event
└─ Label

Channel Summary

Total channels32

EEG32 (Ag/AgCl)

Montage10-05

Sampling1000 Hz

ReferenceCPz

Filter{'sampling_rate': 1000}

Notch / line50 Hz

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

Overview

Visual imagery EEG dataset from Gao et al 2026.

Dataset from the article An EEG Dataset for Visual Imagery-Based Brain-Computer Interface [1].

Note: This is a visual imagery dataset (not motor imagery). Participants imagined visual stimuli across three categories: animals, geometric figures, and everyday objects.

EEG was recorded from 22 healthy participants using a 32-channel Neuracle NeuSenW32 wireless cap at 1000 Hz, with Cpz reference.

Three task categories were recorded per session:

• Animals (AVI): dog, bird, fish (3 classes, 40 trials each)

• Figures (FVI): pentagram, square, circle (3 classes, 40 trials each)

• Objects (OVI): scissor, watch, cup, chair (4 classes, 40 trials each)

Each trial: 3 s fixation, 4 s image presentation, 4 s imagery period (the epoch of interest), then rest. Events mark imagery onset.

Most subjects completed 2 sessions. Subjects 9 and 10 completed only session 1.

References

[1]

Gao, J., Liu, Y., Li, Z., Huang, K., Wang, F., Xu, J., Zhao, L., Li, T., & Fu, Y. (2026). An EEG Dataset for Visual Imagery-Based Brain-Computer Interface. Scientific Data. https://doi.org/10.1038/s41597-025-06512-5

[2]

Gao, J. et al. (2026). EEG Dataset for Visual Imagery. Figshare. https://doi.org/10.6084/m9.figshare.30227503.v1

Code Examples

from moabb.datasets import Gao2026
dataset = Gao2026()
data = dataset.get_data(subjects=[1])
print(data[1])

Metadata

Dataset summary

#Subj	22
#Chan	32
#Classes	10
#Trials / class	40
Trials length	4 s
Freq	1000 Hz
#Sessions	2
#Runs	3
Total_trials	16800

Participants

• Population: healthy

Equipment

• Amplifier: Neuracle NeuSenW32

• Electrodes: Ag/AgCl

• Montage: standard_1005

• Reference: CPz

Data Access

• DOI: 10.1038/s41597-025-06512-5

• Data URL: https://doi.org/10.6084/m9.figshare.30227503.v1

• Repository: Figshare

Experimental Protocol

• Paradigm: imagery

• Feedback: none

• Stimulus: image cues

__init__(subjects=None, 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