moabb.datasets.MartinezPeon2025#

class moabb.datasets.MartinezPeon2025[source]#

Bases: BaseDataset

Dataset Snapshot

MartinezPeon2025

Raw, unfiltered EEG during graded-force kinesthetic motor imagery of hand-grip (10/40/70/100% MVC) from 50 healthy right-handed students, Cognionics Quick-20m, 20 channels, 500 Hz (inferred).

Imagery, 4 classes (grip_10 vs grip_40 vs grip_70 vs grip_100)

AuthorsDulce Citlalli Martinez Peon, Marcos Perez Espinoza

2025

Imagery Code: MartinezPeon2025 50 subjects 1 session 20 ch 500 Hz 4 classes 4.0 s trials CC BY-NC-SA 4.0

Class Labels: grip_10, grip_40, grip_70, grip_100

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Overview

Graded-force kinesthetic motor imagery of hand-grip

Dataset description

Raw EEG recorded during kinesthetic motor imagery (KMI) of a hand-grip at 10%, 40%, 70% and 100% of the maximal voluntary contraction (MVC) from 50 healthy right-handed university students (23 females, 27 males, 17-30 years, mean 21.78 +/- 2.66); subjects S01-S25 used the left hand and S26-S50 the right hand. Each force level is one 84 s recording (10 repetitions of 4 s imagery followed by 4 s rest, with initial and final resting periods; rest corresponds to the 0% level), loaded as one run with 10 trials of its class. EEG was recorded with a 20-channel Cognionics Quick-20m headset (electrodes Fp1, Fp2, F7, F3, Fz, F4, F8, T7, C3, Cz, C4, T8, A2, P7, P3, Pz, P4, P8, O1, O2; the record does not state the reference, and A2 is one of the 20 recorded columns) without filtering or pre-processing. The sampling rate is not stated in the record and is inferred from the 42000 samples per 84 s file (500 Hz). The legacy T3/T4 labels are renamed to T7/T8 and microvolts are converted to volts on load.

The Mendeley record has no linked paper; its summary sentence lists the levels as 0%, 40%, 70% and 100% while its experimental-design section lists 0%, 10%, 40%, 70% and 100% and each subject folder holds four KMI files (10/40/70/100). The dataset has no official name, so the class follows the first-author-surname + record-year rule (Martinez Peon, 2025).

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Citation & Impact

Paper DOI10.17632/msgzn862ns.1
CitationsLoading…
Public APICrossref | OpenAlex

Stimulus Protocol

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

HED Event Tags

HED tags4/4 events annotated

Source: MOABB BIDS HED annotation mapping.

Label

4

Sensory-event

4

grip_10

Sensory-eventLabel

grip_40

Sensory-eventLabel

grip_70

Sensory-eventLabel

grip_100

Sensory-eventLabel

HED tree view

Tree · grip_10

├─ Sensory-event
└─ Label

Tree · grip_40

├─ Sensory-event
└─ Label

Tree · grip_70

├─ Sensory-event
└─ Label

Tree · grip_100

├─ Sensory-event
└─ Label

Channel Summary

Total channels20

EEG20 (dry)

Montage10-20

Sampling500 Hz

Notch / line50 Hz

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

Overview

Graded-force kinesthetic motor imagery of hand-grip [1].

Dataset description

Raw EEG recorded during kinesthetic motor imagery (KMI) of a hand-grip at 10%, 40%, 70% and 100% of the maximal voluntary contraction (MVC) from 50 healthy right-handed university students (23 females, 27 males, 17-30 years, mean 21.78 +/- 2.66); subjects S01-S25 used the left hand and S26-S50 the right hand. Each force level is one 84 s recording (10 repetitions of 4 s imagery followed by 4 s rest, with initial and final resting periods; rest corresponds to the 0% level), loaded as one run with 10 trials of its class. EEG was recorded with a 20-channel Cognionics Quick-20m headset (electrodes Fp1, Fp2, F7, F3, Fz, F4, F8, T7, C3, Cz, C4, T8, A2, P7, P3, Pz, P4, P8, O1, O2; the record does not state the reference, and A2 is one of the 20 recorded columns) without filtering or pre-processing. The sampling rate is not stated in the record and is inferred from the 42000 samples per 84 s file (500 Hz). The legacy T3/T4 labels are renamed to T7/T8 and microvolts are converted to volts on load.

The Mendeley record has no linked paper; its summary sentence lists the levels as 0%, 40%, 70% and 100% while its experimental-design section lists 0%, 10%, 40%, 70% and 100% and each subject folder holds four KMI files (10/40/70/100). The dataset has no official name, so the class follows the first-author-surname + record-year rule (Martinez Peon, 2025).

References

[1]

Martinez Peon, D. C., & Perez Espinoza, M. (2025). EEG signals of KMI levels of the right and left hands. Mendeley Data, V1. DOI: https://doi.org/10.17632/msgzn862ns.1

Code Examples

from moabb.datasets import MartinezPeon2025
dataset = MartinezPeon2025()
data = dataset.get_data(subjects=[1])
print(data[1])

Metadata

Dataset summary

#Subj	50
#Chan	20
#Classes	4
#Trials / class	10
Trials length	3.998 s
Freq	500 Hz
#Sessions	1
#Runs	4
Total_trials	2000

Participants

Population: healthy
Age: 21.78 (range: 17-30) years
Handedness: {‘right’: 50}

Equipment

Amplifier: Cognionics Quick-20m dry-electrode EEG headset
Electrodes: dry
Montage: 10-20

Data Access

DOI: 10.17632/msgzn862ns.1
Data URL: https://data.mendeley.com/datasets/msgzn862ns/1
Repository: Mendeley Data

Experimental Protocol

Paradigm: imagery
Feedback: none

Notes

Added in version 1.1.1.

Each class comes from a single continuous 84 s recording per subject (class==recording-block confound): within-recording CV can exploit recording-level nonstationarity instead of imagery content; prefer cross-subject evaluation.

__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 the four CSV paths of a subject, ordered 10/40/70/100% MVC.

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/.