moabb.datasets.PerezBlanco2026#

class moabb.datasets.PerezBlanco2026(subjects=None, sessions=None, *, return_all_modalities=False)[source]#

Bases: BaseDataset

Dataset Snapshot

PerezBlanco2026

Imagery, 4 classes (flexion vs extension vs radial_deviation vs ulnar_deviation)

AuthorsJorge German Perez Blanco, Javier Mauricio Antelis Ortiz, Luis Guillermo Hernandez Rojas, Hector Lizarraga Torreblanca

🇲🇽 Tecnologico de Monterrey, MX·2026

Imagery Code: PerezBlanco2026 45 subjects 1 session 16 ch (8 EEG) 512 Hz 4 classes 10.0 s trials CC BY 4.0

Class Labels: flexion, extension, radial_deviation, ulnar_deviation

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Overview

Wrist-motion (4-direction pointing) motor-execution dataset

Dataset description

EEG, EMG and wrist-kinematic data from 45 healthy participants (20-83 years, mean 38.5 +/- 19.9; 26 female; 40 right-handed) performing a cursor-control wrist-pointing task with the Biomech Wrist, a 3-DoF wrist rehabilitation exoskeleton worn on the right forearm (motors de-energized, encoders only). Wrist flexion-extension moved the cursor horizontally and radial-ulnar deviation moved it vertically. On each 10 s trial a target appeared in one of four cardinal directions and the participant moved the cursor to it, yielding four balanced movement classes: flexion, extension, radial deviation, ulnar deviation. This is an overt motor-execution task (no imagery condition); the loader declares paradigm="imagery" only so that it runs under MOABB's MotorImagery paradigm, as other execution datasets do.

The paper recommends excluding subject 31 from EEG analyses because of persistently high noise across all channels. Recordings took place at Tecnologico de Monterrey in Guadalajara, Mexico (the BIDS sidecar gives the institution address of the Monterrey campus). The Figshare record licenses the data CC BY 4.0, while dataset_description.json states "License": "PD".

Each trial is 3 s fixation, 2 s target preview, 2.5 s movement execution and 2.5 s return-to-center. Participants completed at least 8 runs of 40 trials; the released run count varies by subject, so :meth:`data_path` loads every task-*_run-* EDF present. Signals (8 EEG, 8 bipolar EMG) were recorded at 512 Hz with BCI2000 and converted to EDF for BIDS.

The trigger information is carried inside the EDF as BCI2000 state channels. This loader places one event at each movement-execution onset (the TrialInitMovStamp rising edge, 5 s into the trial) and reads the movement class from the CurrentTarget state at that sample. The default analysis interval [0, 2.5] s therefore spans the 2.5 s movement-execution phase.

By default only the EEG channels are returned; set return_all_modalities=True to additionally return the 8 EMG channels.

:param subjects: The subjects to load. If None, all 45 subjects are used. :type subjects: list of int | None :param sessions: The sessions to load. If None, the single session is used. :type sessions: list of str | None :param return_all_modalities: If True, also return the 8 EMG channels alongside the EEG channels. :type return_all_modalities: bool

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Citation & Impact

Paper DOI10.1038/s41597-026-07287-z
CitationsLoading…
Public APICrossref | OpenAlex

Stimulus Protocol

10s task window per trial · 4-class imagery paradigm · 8 runs/session across 1 sessions

HED Event Tags

HED tags4/4 events annotated

Source: MOABB BIDS HED annotation mapping.

Label

4

Sensory-event

4

flexion

Sensory-eventLabel

extension

Sensory-eventLabel

radial_deviation

Sensory-eventLabel

ulnar_deviation

Sensory-eventLabel

HED tree view

Tree · flexion

├─ Sensory-event
└─ Label

Tree · extension

├─ Sensory-event
└─ Label

Tree · radial_deviation

├─ Sensory-event
└─ Label

Tree · ulnar_deviation

├─ Sensory-event
└─ Label

Channel Summary

Total channels16

EEG8

EMG8

Montage10-10

Sampling512 Hz

Referenceright ear

Filter{'bandpass': [0.1, 200.0], 'notch': 60.0}

Notch / line60 Hz

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

Overview

Wrist-motion (4-direction pointing) motor-execution dataset [1].

Dataset description

EEG, EMG and wrist-kinematic data from 45 healthy participants (20-83 years, mean 38.5 +/- 19.9; 26 female; 40 right-handed) performing a cursor-control wrist-pointing task with the Biomech Wrist, a 3-DoF wrist rehabilitation exoskeleton worn on the right forearm (motors de-energized, encoders only). Wrist flexion-extension moved the cursor horizontally and radial-ulnar deviation moved it vertically. On each 10 s trial a target appeared in one of four cardinal directions and the participant moved the cursor to it, yielding four balanced movement classes: flexion, extension, radial deviation, ulnar deviation. This is an overt motor-execution task (no imagery condition); the loader declares paradigm="imagery" only so that it runs under MOABB’s MotorImagery paradigm, as other execution datasets do.

The paper recommends excluding subject 31 from EEG analyses because of persistently high noise across all channels. Recordings took place at Tecnologico de Monterrey in Guadalajara, Mexico (the BIDS sidecar gives the institution address of the Monterrey campus). The Figshare record licenses the data CC BY 4.0, while dataset_description.json states "License": "PD".

Each trial is 3 s fixation, 2 s target preview, 2.5 s movement execution and 2.5 s return-to-center. Participants completed at least 8 runs of 40 trials; the released run count varies by subject, so data_path() loads every task-*_run-* EDF present. Signals (8 EEG, 8 bipolar EMG) were recorded at 512 Hz with BCI2000 and converted to EDF for BIDS.

The trigger information is carried inside the EDF as BCI2000 state channels. This loader places one event at each movement-execution onset (the TrialInitMovStamp rising edge, 5 s into the trial) and reads the movement class from the CurrentTarget state at that sample. The default analysis interval [0, 2.5] s therefore spans the 2.5 s movement-execution phase.

By default only the EEG channels are returned; set return_all_modalities=True to additionally return the 8 EMG channels.

param subjects:: The subjects to load. If None, all 45 subjects are used.
type subjects:: list of int | None
param sessions:: The sessions to load. If None, the single session is used.
type sessions:: list of str | None
param return_all_modalities:: If True, also return the 8 EMG channels alongside the EEG channels.
type return_all_modalities:: bool

References

[1]

Perez-Blanco, J. G., Antelis-Ortiz, J. M., Hernandez-Rojas, L. G., & Lizarraga-Torreblanca, H. (2026). An EEG-EMG-kinematics dataset of wrist movements with a rehabilitation exoskeleton. Scientific Data. DOI: https://doi.org/10.1038/s41597-026-07287-z Data: https://doi.org/10.6084/m9.figshare.29666735

Code Examples

from moabb.datasets import PerezBlanco2026
dataset = PerezBlanco2026()
data = dataset.get_data(subjects=[1])
print(data[1])

Metadata

Dataset summary

#Subj	45
#Chan	8
#Classes	4
#Trials / class	80
Trials length	2.5 s
Freq	512 Hz
#Sessions	1
#Runs	8
Total_trials	14400

Participants

Population: healthy
Age: 38.5 (range: 20-83) years
Handedness: {‘right’: 40, ‘left’: 5}

Equipment

Amplifier: g.tec g.USBamp (serial UB-2016.05.01)
Montage: 10-10
Reference: right ear

Data Access

DOI: 10.1038/s41597-026-07287-z
Data URL: https://doi.org/10.6084/m9.figshare.29666735
Repository: Figshare

Experimental Protocol

Paradigm: imagery
Task type: motor_execution
Feedback: continuous visual
Stimulus: visual target

Notes

Added in version 1.8.

__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, 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]#: Return the sorted run EDF paths of subject, downloading if needed.

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.

When the dataset declares a nemar_id and the download provider is not "upstream", the files come from NEMAR’s sourcedata/ – the original pre-BIDS distribution, byte-identical to what the upstream host serves. On any NEMAR failure this falls back to the dataset’s own downloader with a warning (unless the provider is pinned to "nemar"). See sourcedata_path() and moabb.set_download_provider().

Parameters:

subject_list (list of int | None) – List of subjects id to download, if None all subjects are downloaded. On the NEMAR path each subject is resolved through the deposit’s sourcedata_provenance.json; deposits enriched before that manifest recorded subjects fetch the whole tree.
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. Not used on the NEMAR path.
accept (bool) – Accept licence term to download the data, if any. Default: False. Only relevant to the dataset’s own downloader; NEMAR mirrors are already public.
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

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

Get the dataset’s original pre-BIDS distribution from NEMAR.

Where data_path() fetches the original files from the upstream host, this fetches the copy NEMAR mirrors under sourcedata/. The files keep their upstream names, so the two are interchangeable in content – but NEMAR stays reachable when the upstream host is slow, rate-limited, behind a bot gate, or retired.

Parameters:

subject (int | str | None) – Restrict the download to one subject, resolved through the deposit’s sourcedata_provenance.json. Deposits enriched before that manifest recorded subjects fall back to the whole tree with a warning. When None the whole tree is fetched.
path (None | str) – Base path where MOABB stores datasets.
force_update (bool) – Re-fetch even when a local copy is present.
verbose (bool, str, int, or None) – If not None, override default verbose level.

Returns:

Local path to the sourcedata directory.

Return type:

str

Raises:

ValueError – If the dataset declares no nemar_id.
moabb.datasets.download.NemarDownloadError – If the download fails or the deposit publishes no sourcedata/.