moabb.datasets.Lenaig2026#

class moabb.datasets.Lenaig2026(exp=1, run='both', stim='Sinus')[source]#

Bases: BaseDataset

[source]

Dataset Snapshot

Lenaig2026

An enhanced experimental paradigm for auditory BCIs by addressing both acoustic and human factors.

SSVEP, 2 classes (Sinus vs Silence)

AuthorsLenaïg Guého, Laurent Bougrain, Cyril Plapous, Patrick Hénaff, Rozenn Nicol

🇫🇷 Orange Labs, FR·2026
SSVEP Code: Lenaig2026 24 subjects 1 session 28 ch (24 EEG) 500 Hz 2 classes 10.0 s trials CC BY-NC-ND 4.0

Class Labels: Sinus, Silence

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Overview

SSAEP BCI dataset from Guého et al. (2026).

Dataset from the paper

Dataset Description

This study investigates the EEG activity of 48 participants in response to a set of four auditory stimuli: a pure tone (used as a reference), cicada song and cat's purr (natural sounds), and brownian noise (an artificial signal with a spectral content similar to that of certain natural sounds, such as water noise). In addition, a silent condition, corresponding to the absence of auditory stimulation, was included for subsequent analyses. The stimuli are amplitude-modulated by a 40 Hz sinusoid (modulation index = 1) and have a duration of 10 seconds. The experiment is conducted at two loudness levels (60 and 66 phons, diotic presentation), with 24 participants each.

EEG acquisition is performed using a 24-channel mBrainTrain headset (international 10-20 system, passive electrodes, impedance maintained below 10 kΩ), coupled with a Smarting module, at a sampling rate of 500 Hz.

The measurement consists in one session of two 10-minute runs (separated by a 5-minute break), each including 50 trials (10 repetitions per condition), spaced by intervals ranging from 6 to 10 seconds. Stimuli are presented in a pseudo-random order to avoid immediate repetitions of the same stimulus and to prevent a stimulus ending a sequence of five trials from starting the next sequence.

:param exp: Experiment number (1 or 2). Default is 1. :type exp: int, optional :param run: Which run(s) to use (1, 2, or 'both'). Default is 'both'. :type run: int, str, optional :param stim: Stimulus type ('Sinus', 'BrownNoise', 'Cat', 'Cicada'). Default is 'Sinus'. :type stim: str, optional

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Citation & Impact

Stimulus Protocol
../_images/Lenaig2026.svg

10s task window per trial · 2-class ssvep paradigm · 1 runs/session across 1 sessions

HED Event Tags
HED tags2/2 events annotated

Source: MOABB BIDS HED annotation mapping.

Experimental-stimulus
2
Label
2
Sensory-event
2
Visual-presentation
2
Sinus
Sensory-eventExperimental-stimulusVisual-presentationLabel
Silence
Sensory-eventExperimental-stimulusVisual-presentationLabel

HED tree view

Tree · Sinus
├─ Sensory-event
├─ Experimental-stimulus
├─ Visual-presentation
└─ Label
Tree · Silence
├─ Sensory-event
├─ Experimental-stimulus
├─ Visual-presentation
└─ Label
Channel Summary
Total channels28
EEG24 (EEG)
MISC3
Montagestandard_1020
Sampling500 Hz
Notch / line50 Hz

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

SSAEP BCI dataset from Guého et al. (2026).

Dataset from the paper [1].

Dataset Description

This study investigates the EEG activity of 48 participants in response to a set of four auditory stimuli: a pure tone (used as a reference), cicada song and cat’s purr (natural sounds), and brownian noise (an artificial signal with a spectral content similar to that of certain natural sounds, such as water noise). In addition, a silent condition, corresponding to the absence of auditory stimulation, was included for subsequent analyses. The stimuli are amplitude-modulated by a 40 Hz sinusoid (modulation index = 1) and have a duration of 10 seconds. The experiment is conducted at two loudness levels (60 and 66 phons, diotic presentation), with 24 participants each.

EEG acquisition is performed using a 24-channel mBrainTrain headset (international 10-20 system, passive electrodes, impedance maintained below 10 kΩ), coupled with a Smarting module, at a sampling rate of 500 Hz.

The measurement consists in one session of two 10-minute runs (separated by a 5-minute break), each including 50 trials (10 repetitions per condition), spaced by intervals ranging from 6 to 10 seconds. Stimuli are presented in a pseudo-random order to avoid immediate repetitions of the same stimulus and to prevent a stimulus ending a sequence of five trials from starting the next sequence.

param exp:

Experiment number (1 or 2). Default is 1.

type exp:

int, optional

param run:

Which run(s) to use (1, 2, or ‘both’). Default is ‘both’.

type run:

int, str, optional

param stim:

Stimulus type (‘Sinus’, ‘BrownNoise’, ‘Cat’, ‘Cicada’). Default is ‘Sinus’.

type stim:

str, optional

References

[1]

  1. Guého, L. Bougrain, C. Plapous, P. Hénaff and R. Nicol, “An enhanced experimental paradigm for auditory BCIs by addressing both acoustic and human factors,” 2026 14th International Conference on Brain-Computer Interface (BCI), Gangwon Province, Korea, Republic of, 2026, pp. 1-7, doi: 10.1109/BCI69045.2026.11435072.

from moabb.datasets import Lenaig2026
dataset = Lenaig2026()
data = dataset.get_data(subjects=[1])
print(data[1])

Dataset summary

#Subj

24

#Chan

24

#Classes

2

#Trials / class

10

Trials length

10 s

Freq

500 Hz

#Sessions

1

Participants

  • Population: healthy

Equipment

  • Electrodes: EEG

  • Montage: standard_1020

Data Access

  • DOI: 10.1109/BCI69045.2026.11435072

Experimental Protocol

  • Paradigm: ssvep

  • Feedback: auditory

__init__(exp=1, run='both', stim='Sinus')[source]#

Initialize the Lenaig2026 dataset object.

Parameters:

  • exp (int, optional) – Experiment number (1 or 2). Default is 1.

  • run (int, str, optional) – Which run(s) to use (1, 2, or ‘both’). Default is ‘both’.

  • stim (str, optional) – Stimulus type (‘Sinus’, ‘BrownNoise’, ‘Cat’, ‘Cicada’). Default is ‘Sinus’.

Raises:

ValueError – If an invalid experiment or run is provided.

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, generate_figures=False)[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.

  • generate_figures (bool) – If True, generate interactive neural signature HTML figures in {bids_root}/derivatives/neural_signatures/. Requires plotly (pip install moabb[interactive]). Default is False.

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])

Notes

Use CacheConfig to configure caching for get_data(). Use moabb.datasets.bids_interface.get_bids_root to get the BIDS root path.

Added in version 1.5.

data_path(subject, path=None, force_update=False, update_path=None, verbose=None)[source]#

Get the list of file paths for a given subject and selected runs.

Parameters:

  • subject (int) – Subject number.

  • path (str or Path, optional) – Base directory for data. If None, uses DATA_DIR.

  • force_update (bool, optional) – Redownload the data.

  • update_path (str, optional) – Unused, for compatibility.

  • verbose (bool, optional) – Unused, for compatibility.

Returns:

List of file paths for the subject and selected runs.

Return type:

list of Path

Raises:
download(subject_list=None, path=None, force_update=False, update_path=None, accept=False, verbose=None)[source]#

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)[source]#

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 | pandas.DataFrame

get_block_repetition(paradigm, subjects, block_list, repetition_list)[source]#

Select data for all provided subjects, blocks and repetitions.

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

See also

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, n_jobs=1)[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 (sklearn.pipeline.Pipeline | None) – Optional processing pipeline to apply to the data. To generate an adequate pipeline, we recommend using moabb.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.

  • n_jobs (int) – Number of jobs to run in parallel over subjects (passed to joblib.Parallel). Default 1 (sequential). Per-subject processing (reading, filtering, resampling, epoching) is independent, so this gives a near-linear speedup for datasets with many subjects.

Returns:

data – dict containing the raw data

Return type:

Dict

property metadata[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