moabb.datasets.MOVING2024#

class moabb.datasets.MOVING2024(execution=False, subjects=None, sessions=None)[source]#

Bases: BaseDataset

[source]

Dataset Snapshot

MOVING2024

Multi-modal dataset pairing 32-channel dry EEG with Virtual Glove hand-kinematic tracking during motor imagery and motor execution of three right-hand movements.

Imagery, 4 classes (rest vs open_close vs wrist_rotation vs finger_tapping)

AuthorsEnrico Mattei, Daniele Lozzi, Alessandro Di Matteo, Alessia Cipriani, Costanzo Manes, Giuseppe Placidi

🇮🇹 University of L'Aquila, IT·2024
Imagery Code: MOVING2024 11 subjects 1 session 35 ch (32 EEG) 500 Hz 4 classes 6.0 s trials CC BY 4.0

Class Labels: rest, open_close, wrist_rotation, finger_tapping

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Overview

Motor imagery / motor execution dataset from the MOVING study

Dataset description

The MOVING dataset couples 32-channel dry-electrode EEG with hand-kinematic tracking from a Virtual Glove (two orthogonal Leap Motion Controllers). Eleven healthy participants performed three right-hand movements -- open/close, wrist rotation and finger tapping -- each preceded by a rest baseline and performed both as motor imagery (MI) and as motor execution (ME).

Each subject completed a single continuous ~10-minute recording made of 8 repetitions of a fixed block. Within a block, each of the three movements is presented as a rest -> MI -> ME triplet, and the movements always appear in the same order (open/close, wrist rotation, finger tapping). Every 6 s action period is preceded by a 2 s fixation cross, giving the trigger stream:

======== ================= ========== Trigger Phase Class ======== ================= ========== #1 rest (open/close) rest #3 MI open/close open_close #5 ME open/close open_close #7 rest (wrist) rest #9 MI wrist rotation wrist_rotation #11 ME wrist rotation wrist_rotation #13 rest (finger) rest #15 MI finger tapping finger_tapping #17 ME finger tapping finger_tapping ======== ================= ==========

The even triggers (#2, #4, ... #16) mark the 2 s fixation crosses and are not used as classes. Each odd trigger occurs 8 times (once per block), so every movement contributes 8 MI and 8 ME trials, and the shared rest class contributes 24 trials.

The default paradigm exposes the four motor-imagery classes (rest, open_close, wrist_rotation, finger_tapping). Pass execution=True to expose the matching four motor-execution classes instead. EEG is recorded with a dry 32-channel Enobio system (10-20 layout) at 500 Hz; the three trailing accelerometer axes are kept as misc channels.

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Citation & Impact

Stimulus Protocol
../_images/MOVING2024.svg

6s task window per trial · 4-class imagery paradigm · 1 runs/session across 1 sessions

HED Event Tags
HED tags4/4 events annotated

Source: MOABB BIDS HED annotation mapping.

Sensory-event
4
Label
3
Experimental-stimulus
1
Rest
1
Visual-presentation
1
rest
Sensory-eventExperimental-stimulusVisual-presentationRest
open_close
Sensory-eventLabel
wrist_rotation
Sensory-eventLabel
finger_tapping
Sensory-eventLabel

HED tree view

Tree · rest
├─ Sensory-event
├─ Experimental-stimulus
├─ Visual-presentation
└─ Rest
Tree · open_close
├─ Sensory-event
└─ Label
Tree · wrist_rotation
├─ Sensory-event
└─ Label
Tree · finger_tapping
├─ Sensory-event
└─ Label
Channel Summary
Total channels35
EEG32 (dry)
MISC3
Montage10-20
Sampling500 Hz
ReferenceCMS/DRL
Notch / line50 Hz

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

Motor imagery / motor execution dataset from the MOVING study [1] [2].

Dataset description

The MOVING dataset couples 32-channel dry-electrode EEG with hand-kinematic tracking from a Virtual Glove (two orthogonal Leap Motion Controllers). Eleven healthy participants performed three right-hand movements – open/close, wrist rotation and finger tapping – each preceded by a rest baseline and performed both as motor imagery (MI) and as motor execution (ME).

Each subject completed a single continuous ~10-minute recording made of 8 repetitions of a fixed block. Within a block, each of the three movements is presented as a rest -> MI -> ME triplet, and the movements always appear in the same order (open/close, wrist rotation, finger tapping). Every 6 s action period is preceded by a 2 s fixation cross, giving the trigger stream:

Trigger

Phase

Class

#1

rest (open/close)

rest

#3

MI open/close

open_close

#5

ME open/close

open_close

#7

rest (wrist)

rest

#9

MI wrist rotation

wrist_rotation

#11

ME wrist rotation

wrist_rotation

#13

rest (finger)

rest

#15

MI finger tapping

finger_tapping

#17

ME finger tapping

finger_tapping

The even triggers (#2, #4, … #16) mark the 2 s fixation crosses and are not used as classes. Each odd trigger occurs 8 times (once per block), so every movement contributes 8 MI and 8 ME trials, and the shared rest class contributes 24 trials.

The default paradigm exposes the four motor-imagery classes (rest, open_close, wrist_rotation, finger_tapping). Pass execution=True to expose the matching four motor-execution classes instead. EEG is recorded with a dry 32-channel Enobio system (10-20 layout) at 500 Hz; the three trailing accelerometer axes are kept as misc channels.

References

[1]

Mattei, E., Lozzi, D., Di Matteo, A., Cipriani, A., Manes, C., & Placidi, G. (2024). MOVING: A Multi-Modal Dataset of EEG Signals and Virtual Glove Hand Tracking. Sensors, 24(16), 5207. DOI: https://doi.org/10.3390/s24165207

[2]

Mattei, E., Lozzi, D., Di Matteo, A., Placidi, G., Manes, C., & Cipriani, A. (2024). MOVING dataset [Data set]. Zenodo. DOI: https://doi.org/10.5281/zenodo.12804784

from moabb.datasets import MOVING2024
dataset = MOVING2024()
data = dataset.get_data(subjects=[1])
print(data[1])

Dataset summary

#Subj

11

#Chan

32

#Classes

4

#Trials / class

12

Trials length

6 s

Freq

500 Hz

#Sessions

1

#Runs

1

Total_trials

528

Participants

  • Population: healthy

Equipment

  • Amplifier: Neuroelectrics Enobio 32 (dry electrodes, wireless)

  • Electrodes: dry

  • Montage: 10-20

  • Reference: CMS/DRL

Data Access

Experimental Protocol

  • Paradigm: imagery

  • Stimulus: visual

Notes

The fixed movement order can confound class with position within a block; random trial-level splits do not remove this protocol limitation. Rest has 24 trials versus 8 for each movement (48 trials per subject).

Extraction of edf.rar requires unrar, unar or 7z to be installed on the system.

The Sensors paper describes the protocol (32 dry Enobio electrodes, 2 s fixation + 6 s action, eight repetitions of the triplet, ~10 min) but states neither the number of participants nor the raw sampling rate; the eleven subjects come from the Zenodo record and the 500 Hz rate from the EDF headers (paper audit, 2026-09-30).

Added in version 1.8.

__init__(execution=False, 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]#

Return [edf_path], downloading and extracting edf.rar 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/.