moabb.datasets.AguileraRodriguez2025#

class moabb.datasets.AguileraRodriguez2025(subjects=None, sessions=None)[source]#

Bases: BaseDataset

Dataset Snapshot

AguileraRodriguez2025

EEG-based imagined speech database comparing traditional cue-based and gamified (Pac-man) paradigms. 4 Spanish directional words. Ethics: CONBIOETICA-19-CEI-011-20161017. Paper reports 32.48% (traditional) and 35.65% (gamified) accuracy with Random Forest.

Imagery, 4 classes (avanzar vs retroceder vs derecha vs izquierda)

AuthorsEdgar Aguilera-Rodriguez, Alma Cuevas-Romero, Santiago Mendoza-Franco, Jonathan Wornovitzky-Green, Eduardo Rivera-Cerros, David Villanueva-Cazares, Luis Alberto Munoz-Ubando, David Ibarra-Zarate, Luz Maria Alonso-Valerdi

🇲🇽 Tecnologico de Monterrey, MX·2025

Imagery Code: AguileraRodriguez2025 15 subjects 1 session 24 ch 500 Hz 4 classes 11.8 s trials CC BY-NC-ND 4.0

Class Labels: avanzar, retroceder, derecha, izquierda

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Overview

Imagined Speech EEG dataset comparing paradigm designs.

Dataset from Aguilera-Rodriguez et al., published in Scientific Data.

Dataset Description

Fifteen participants (8 male, 7 female, ages 18-27) performed imagined speech of four Spanish directional words: "avanzar" (advance), "retroceder" (backwards), "derecha" (right), "izquierda" (left).

Two paradigms were used:

- Traditional (session 0): Cue-based design built with OpenViBE. EEG stored as EDF files with annotation markers.
Gamified (session 1): Video-game (Pac-man maze) design built with Pygame/LSL. EEG stored as XDF files.

EEG was recorded at 500 Hz with 24 channels using mBrainTrain Smarting (FCz reference, Fpz ground). Each paradigm has 120 trials (30 per word).

Open in Overview tab →

Citation & Impact

Paper DOI10.1038/s41597-025-05926-5
CitationsLoading…
Public APICrossref | OpenAlex

HED Event Tags

HED tags4/4 events annotated

Source: MOABB BIDS HED annotation mapping.

Agent-action

4

Sensory-event

4

avanzar

Sensory-eventAgent-action

retroceder

Sensory-eventAgent-action

derecha

Sensory-eventAgent-action

izquierda

Sensory-eventAgent-action

HED tree view

Tree · avanzar

├─ Sensory-event
│  ├─ Experimental-stimulus
│  ├─ Visual-presentation
│  └─ Auditory-presentation
└─ Agent-action
   └─ Imagine
      ├─ Speak
      └─ Word
         └─ Label

Tree · retroceder

├─ Sensory-event
│  ├─ Experimental-stimulus
│  ├─ Visual-presentation
│  └─ Auditory-presentation
└─ Agent-action
   └─ Imagine
      ├─ Speak
      └─ Word
         └─ Label

Tree · derecha

├─ Sensory-event
│  ├─ Experimental-stimulus
│  ├─ Visual-presentation
│  └─ Auditory-presentation
└─ Agent-action
   └─ Imagine
      ├─ Speak
      └─ Word
         └─ Label

Tree · izquierda

├─ Sensory-event
│  ├─ Experimental-stimulus
│  ├─ Visual-presentation
│  └─ Auditory-presentation
└─ Agent-action
   └─ Imagine
      ├─ Speak
      └─ Word
         └─ Label

Channel Summary

Total channels24

EEG24 (EEG)

Montage10-05

Sampling500 Hz

ReferenceFCz

Notch / line60 Hz

Overview

Imagined Speech EEG dataset comparing paradigm designs.

Dataset from Aguilera-Rodriguez et al. [1], published in Scientific Data.

Dataset Description

Fifteen participants (8 male, 7 female, ages 18-27) performed imagined speech of four Spanish directional words: “avanzar” (advance), “retroceder” (backwards), “derecha” (right), “izquierda” (left).

Two paradigms were used:

Traditional (session 0): Cue-based design built with OpenViBE. EEG stored as EDF files with annotation markers.
Gamified (session 1): Video-game (Pac-man maze) design built with Pygame/LSL. EEG stored as XDF files.

EEG was recorded at 500 Hz with 24 channels using mBrainTrain Smarting (FCz reference, Fpz ground). Each paradigm has 120 trials (30 per word).

Note

Only the traditional paradigm (EDF) is loaded by default. The gamified paradigm uses XDF format which requires pyxdf.

AguileraRodriguez2025 trial structure — written word cue + 7 imagined-speech repetitions at T=1.4 s, then 2 s rest. — Figure 1 of [1] (CC-BY-NC-ND-4.0). Recommended bandpass: 1-100 Hz — see `SpeechImagery`.#

References

[1] (1,2)

Aguilera-Rodriguez, E., Cuevas-Romero, A., Mendoza-Franco, S., Wornovitzky-Green, J., Rivera-Cerros, E., Villanueva-Cazares, D., Munoz-Ubando, L. A., Ibarra-Zarate, D., & Alonso-Valerdi, L. M. (2025). An EEG-based Imagined Speech Database for comparing Paradigm Designs. Scientific Data, 12, 1644. https://doi.org/10.1038/s41597-025-05926-5

Code Examples

from moabb.datasets import AguileraRodriguez2025
dataset = AguileraRodriguez2025()
data = dataset.get_data(subjects=[1])
print(data[1])

Metadata

Dataset summary

#Subj	15
#Chan	24
#Classes	4
#Trials / class	30
Trials length	4 s
Freq	500 Hz
#Sessions	1
#Runs	1
Total_trials	1800

Participants

Population: healthy
BCI experience: naive

Equipment

Amplifier: mBrainTrain Smarting (Belgrade, Serbia)
Electrodes: EEG
Montage: standard_1005
Reference: FCz

Preprocessing

Data state: raw

Data Access

DOI: 10.1038/s41597-025-05926-5
Data URL: https://data.mendeley.com/datasets/57g8z63tmy/1
Repository: Mendeley Data

Experimental Protocol

Paradigm: imagery
Stimulus: visual + auditory cue

__init__(subjects=None, sessions=None)[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, 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 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)[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)[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.

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