moabb.datasets.PardoGarcia2026#

class moabb.datasets.PardoGarcia2026[source]#

Bases: BaseDataset

[source]

Dataset Snapshot

PardoGarcia2026

Pre-execution, two-class hand motor-imagery EEG (pinch vs fist) from recordings that continue into cued overt execution: 10 chronic MCA stroke patients (baseline and post-rehabilitation) and 8 healthy controls, 63 BrainVision channels at 1000 Hz.

Imagery, 2 classes (pinch vs fist)

AuthorsRebeca Pardo-Garcia, Maria Ruiz-Izquierdo, Mercedes Garcia de la Vega, Rocio Calvillo, George Kontaxakis, Eva M. Moreno, M. A. Pozo

🇪🇸 Instituto Pluridisciplinar, Universidad Complutense de Madrid; Universidad Politecnica de Madrid; Hospital Clinico San Carlos, ES·2026
Imagery Code: PardoGarcia2026 18 subjects 1 session 63 ch (59 EEG) 1000 Hz 2 classes 1.5 s trials CC BY 4.0

Class Labels: pinch, fist

Read Paper Compare Similar Report Issue

Overview

Mu/beta motor-imagery EEG in chronic MCA stroke and healthy controls

Dataset description

EEG recorded during a cued two-class hand motor-imagery task used to study mu (8-12 Hz) and beta (12-30 Hz) changes in 10 chronic middle cerebral artery (MCA) stroke patients (PAC01-PAC10) and 8 healthy controls (C01, 02, C03, CONTROL04-CONTROL08), with a 63-channel BrainVision system at 1000 Hz. Each trial shows an image of the grip to imagine, a precision pinch (pinch, code 1) or a closed fist (fist, code 2), as documented in the record file bdf_IMAGEN.txt.

Patients were recorded at baseline (session pre) and after rehabilitation (session post), except patient 2 (lost to follow-up); controls have pre only. Each session is one continuous run. The four bipolar EOG channels are typed eog and standard 10-05 template positions are attached. An auditory go-cue marker (S 11/S 22, earliest at 1.510 s) starts overt execution, so this loader maps only the image cues (S 1/S 2) and exposes 0-1.5 s, keeping one event per trial and excluding overt movement. Control recordings hold 50 trials per class, patient recordings about 70.

Paper-vs-release note: the record description reports "7 right-handed healthy controls" and "a 64-channel system (10-20 international system)", while the released files hold eight control recordings (C01, 02, C03, CONTROL04-CONTROL08) and 63 channels in their BrainVision headers (59 EEG incl. A1 + 4 EOG). The companion preprint on the same cohort describes "a 64-channel Ag/AgCl electrode cap (Electro-Cap International), following the international 10-20 system, with A2 as reference" and four ocular electrodes, sampled at 1000 Hz; the record's "140 trials per subject" matches the patient recordings. Recordings were made at the Instituto Pluridisciplinar, Universidad Complutense de Madrid.

Open in Overview tab →

Citation & Impact

Stimulus Protocol
../_images/PardoGarcia2026.svg

1.5s task window per trial · 2-class imagery paradigm · 1 runs/session across 1 sessions

HED Event Tags
HED tags2/2 events annotated

Source: MOABB BIDS HED annotation mapping.

Label
2
Sensory-event
2
pinch
Sensory-eventLabel
fist
Sensory-eventLabel

HED tree view

Tree · pinch
├─ Sensory-event
└─ Label
Tree · fist
├─ Sensory-event
└─ Label
Channel Summary
Total channels63
EEG59 (Ag/AgCl)
EOG4
Montage10-10
Sampling1000 Hz
ReferenceA2 (right mastoid)
Notch / line50 Hz

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

Mu/beta motor-imagery EEG in chronic MCA stroke and healthy controls [1].

Dataset description

EEG recorded during a cued two-class hand motor-imagery task used to study mu (8-12 Hz) and beta (12-30 Hz) changes in 10 chronic middle cerebral artery (MCA) stroke patients (PAC01-PAC10) and 8 healthy controls (C01, 02, C03, CONTROL04-CONTROL08), with a 63-channel BrainVision system at 1000 Hz. Each trial shows an image of the grip to imagine, a precision pinch (pinch, code 1) or a closed fist (fist, code 2), as documented in the record file bdf_IMAGEN.txt.

Patients were recorded at baseline (session pre) and after rehabilitation (session post), except patient 2 (lost to follow-up); controls have pre only. Each session is one continuous run. The four bipolar EOG channels are typed eog and standard 10-05 template positions are attached. An auditory go-cue marker (S 11/S 22, earliest at 1.510 s) starts overt execution, so this loader maps only the image cues (S 1/S 2) and exposes 0-1.5 s, keeping one event per trial and excluding overt movement. Control recordings hold 50 trials per class, patient recordings about 70.

Paper-vs-release note: the record description reports “7 right-handed healthy controls” and “a 64-channel system (10-20 international system)”, while the released files hold eight control recordings (C01, 02, C03, CONTROL04-CONTROL08) and 63 channels in their BrainVision headers (59 EEG incl. A1 + 4 EOG). The companion preprint on the same cohort describes “a 64-channel Ag/AgCl electrode cap (Electro-Cap International), following the international 10-20 system, with A2 as reference” and four ocular electrodes, sampled at 1000 Hz; the record’s “140 trials per subject” matches the patient recordings. Recordings were made at the Instituto Pluridisciplinar, Universidad Complutense de Madrid.

References

[1]

Pardo-Garcia, R., Ruiz-Izquierdo, M., Garcia de la Vega, M., Calvillo, R., Kontaxakis, G., Moreno, E. M., and Pozo, M. A. (2026). Mu and Beta Oscillatory Changes during a motor task following Rehabilitation in Chronic MCA Stroke: Insights from EEG. Zenodo. DOI: https://doi.org/10.5281/zenodo.19599465

from moabb.datasets import PardoGarcia2026
dataset = PardoGarcia2026()
data = dataset.get_data(subjects=[1])
print(data[1])

Dataset summary

#Subj

18

#Chan

59

#Classes

2

#Trials / class

96.11

Trials length

1.5 s

Freq

1000 Hz

#Sessions

1-2

#Runs

1

Total_trials

3460

Participants

  • Population: mixed

  • Clinical population: chronic middle cerebral artery (MCA) stroke

Equipment

  • Amplifier: BrainVision (Brain Products GmbH)

  • Electrodes: Ag/AgCl

  • Montage: 10-10

  • Reference: A2 (right mastoid)

Data Access

Experimental Protocol

  • Paradigm: imagery

  • Feedback: none

  • Stimulus: visual

Notes

Added in version 1.8.0.

__init__()[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 one .vhdr path per session (pre then post).

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/.