moabb.datasets.MILimbEEG#

class moabb.datasets.MILimbEEG[source]#

Bases: BaseDataset

Dataset Snapshot

MILimbEEG

Over 8,680 four-second EEG recordings from 60 volunteers performing and imagining upper- and lower-limb movements, recorded with a 16-channel OpenBCI Cyton+Daisy at 125 Hz.

Imagery, 8 classes

AuthorsVictor Asanza, Daniel Montoya, Leandro L. Lorente-Leyva, Diego H. Peluffo-Ordonez, Kleber Gonzalez

🇪🇨 Escuela Superior Politecnica del Litoral (ESPOL), EC·2023

Imagery Code: MILimbEEG 60 subjects 1 session 16 ch 125 Hz 8 classes 4.0 s trials CC BY 4.0

Class Labels: beo, clh, crh, dlf, plf, drf, prf, rest

Read Paper Compare Similar Report Issue

Overview

Motor and motor-imagery limb EEG dataset (MILimbEEG)

Dataset description

Over 8,680 four-second EEG recordings from 60 adult volunteers of Ecuadorian nationality (average age 36 years; 31 females, 29 males; three left-handed), recruited among ESPOL colleagues and patients of a neurosurgeon at the Hospital Luis Vernaza in Guayaquil, Ecuador. The cohort is not uniformly healthy: two participants have amputations (both upper limbs; right lower limb below the knee), one has hydrocephalus after a ventricular infarct and eighteen are post-COVID-19. EEG was acquired with a 16-channel OpenBCI Cyton+Daisy (dry electrodes, monopolar against a neutral electrode on both ear lobes) at 125 Hz, hardware band-pass filtered 5-50 Hz with a 60 Hz notch. In each repetition (one for most subjects; only one subject performed up to four), participants first executed (M) and then imagined (I) hand closing and foot dorsal/plantar flexion, each task presented randomly up to five times per run, plus a baseline-eyes-open and rest trials. This loader exposes the motor-imagery files only, one session per repetition: the per-trial CSVs (microvolts, converted to volts) are concatenated into a continuous recording with a cue annotation at every trial onset and a non-rejecting EDGE boundary annotation at every join.

Open in Overview tab →

Citation & Impact

Paper DOI10.1016/j.dib.2023.109540
CitationsLoading…
Public APICrossref | OpenAlex

Stimulus Protocol

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

HED Event Tags

HED tags8/8 events annotated

Source: MOABB BIDS HED annotation mapping.

Sensory-event

8

Label

7

Experimental-stimulus

1

Rest

1

Visual-presentation

1

rest

Sensory-eventExperimental-stimulusVisual-presentationRest

beo

Sensory-eventLabel

clh

Sensory-eventLabel

crh

Sensory-eventLabel

dlf

Sensory-eventLabel

plf

Sensory-eventLabel

drf

Sensory-eventLabel

prf

Sensory-eventLabel

HED tree view

Tree · rest

├─ Sensory-event
├─ Experimental-stimulus
├─ Visual-presentation
└─ Rest

Tree · beo

├─ Sensory-event
└─ Label

Tree · clh

├─ Sensory-event
└─ Label

Tree · crh

├─ Sensory-event
└─ Label

Tree · dlf

├─ Sensory-event
└─ Label

Tree · plf

├─ Sensory-event
└─ Label

Tree · drf

├─ Sensory-event
└─ Label

Tree · prf

├─ Sensory-event
└─ Label

Channel Summary

Total channels16

EEG16 (dry)

Montage10-10

Sampling125 Hz

Referenceneutral ear (monopolar)

Notch / line60 Hz

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

Overview

Motor and motor-imagery limb EEG dataset (MILimbEEG) [1] [2].

Dataset description

Over 8,680 four-second EEG recordings from 60 adult volunteers of Ecuadorian nationality (average age 36 years; 31 females, 29 males; three left-handed), recruited among ESPOL colleagues and patients of a neurosurgeon at the Hospital Luis Vernaza in Guayaquil, Ecuador. The cohort is not uniformly healthy: two participants have amputations (both upper limbs; right lower limb below the knee), one has hydrocephalus after a ventricular infarct and eighteen are post-COVID-19. EEG was acquired with a 16-channel OpenBCI Cyton+Daisy (dry electrodes, monopolar against a neutral electrode on both ear lobes) at 125 Hz, hardware band-pass filtered 5-50 Hz with a 60 Hz notch. In each repetition (one for most subjects; only one subject performed up to four), participants first executed (M) and then imagined (I) hand closing and foot dorsal/plantar flexion, each task presented randomly up to five times per run, plus a baseline-eyes-open and rest trials. This loader exposes the motor-imagery files only, one session per repetition: the per-trial CSVs (microvolts, converted to volts) are concatenated into a continuous recording with a cue annotation at every trial onset and a non-rejecting EDGE boundary annotation at every join.

Warning

The Mendeley subject folders (S1 .. S60) are not individually addressable; the whole-record archive is served through a session-signed URL and may require manual retrieval.

References

[1]

Asanza, V., Montoya, D., Lorente-Leyva, L. L., Peluffo-Ordonez, D. H., & Gonzalez, K. (2023). MILimbEEG: A dataset of EEG signals related to upper and lower limb execution of motor and motor imagery tasks. Data in Brief, 50, 109540. DOI: https://doi.org/10.1016/j.dib.2023.109540

[2]

Asanza, V., Montoya, D., Lorente-Leyva, L. L., Peluffo-Ordonez, D. H., & Gonzalez, K. (2022). MILimbEEG. Mendeley Data, V2. DOI: https://doi.org/10.17632/x8psbz3f6x.2

Code Examples

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

Metadata

Dataset summary

#Subj	60
#Chan	16
#Classes	8
Trials length	3.992 s
Freq	125 Hz
#Sessions	1
#Runs	1

Participants

Population: mixed
Clinical population: mostly healthy; 2 amputees (both upper limbs; right lower limb below the knee), 1 hydrocephalus after ventricular infarct, 18 post-COVID-19
Age: 36 years
Handedness: {‘right’: 57, ‘left’: 3}

Equipment

Amplifier: OpenBCI Cyton+Daisy
Electrodes: dry
Montage: 10-10
Reference: neutral ear (monopolar)

Preprocessing

Data state: raw
Bandpass filter: 5-50 Hz
Steps: hardware band-pass 5-50 Hz, 60 Hz notch

Data Access

DOI: 10.1016/j.dib.2023.109540
Data URL: https://doi.org/10.17632/x8psbz3f6x.2
Repository: Mendeley Data

Experimental Protocol

Paradigm: imagery
Feedback: none
Stimulus: visual

Notes

Added in version 1.2.1.

__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]#

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.

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/.