moabb.datasets.NETBCI2026#

class moabb.datasets.NETBCI2026(subjects=None, sessions=None, *, return_all_modalities=False)[source]#

Bases: BaseBIDSDataset

Dataset Snapshot

NETBCI2026

Imagery, 2 classes (right_hand vs rest)

AuthorsMarie-Constance Corsi, Christophe Gitton, Juliana Gonzalez-Astudillo, Ari E. Kahn, Laurent Hugueville, Denis Schwartz, Nathalie George, Mario Chavez, Sophie Dupont, Danielle S. Bassett, Fabrizio De Vico Fallani

🇫🇷 Paris Brain Institute (ICM), FR·2026

Imagery Code: NETBCI2026 19 subjects 4 sessions 74 ch 250 Hz 2 classes 6.0 s trials CC BY 4.0

Class Labels: right_hand, rest

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Overview

NETBCI: longitudinal right-hand motor imagery vs rest BCI training

Networks for BCI (NETBCI), Paris Brain Institute: 19 healthy right-handed adults trained a one-dimensional, two-target cursor task over four sessions on four days, six online feedback runs per session. The up target is reached by sustained kinesthetic motor imagery of right-hand grasping (right_hand), the down target by resting with eyes open (rest). A trial is a 1 s inter-stimulus interval then a 5 s target presentation; events.tsv onsets mark the target and the epoch covers those 5 s. Only the 74-channel EEG (recorded at 1 kHz with simultaneous MEG, both released downsampled to 250 Hz) is exposed. The six runs are the protocol's testing phase (the paper also describes five feedback-free training runs per session used to calibrate the classifier); the resting-state recordings (task-rest) are not loaded.

The released events keep the authors' checked trials (29-32 per run, 14,431 in total). The last rest cue of sub-03 ses-01 run-03 ends after the recording, so :class:`~moabb.paradigms.MotorImagery` returns 14,430 epochs.

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Citation & Impact

Paper DOI10.1038/s41597-026-08237-5
CitationsLoading…
Public APICrossref | OpenAlex

Stimulus Protocol

6s task window per trial · 2-class imagery paradigm · 6 runs/session across 4 sessions

HED Event Tags

HED tags2/2 events annotated

Source: MOABB BIDS HED annotation mapping.

Sensory-event

2

Agent-action

1

Experimental-stimulus

1

Rest

1

Visual-presentation

1

right_hand

Sensory-eventAgent-action

rest

Sensory-eventExperimental-stimulusVisual-presentationRest

HED tree view

Tree · right_hand

├─ Sensory-event
│  ├─ Experimental-stimulus
│  └─ Visual-presentation
└─ Agent-action
   └─ Imagine
      ├─ Move
      └─ Right
         └─ Hand

Tree · rest

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

Channel Summary

Total channels74

EEG74

Montage10-05

Sampling250 Hz

Referencemastoids

Notch / line50 Hz

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

Overview

NETBCI: longitudinal right-hand motor imagery vs rest BCI training [1].

Networks for BCI (NETBCI), Paris Brain Institute: 19 healthy right-handed adults trained a one-dimensional, two-target cursor task over four sessions on four days, six online feedback runs per session. The up target is reached by sustained kinesthetic motor imagery of right-hand grasping (right_hand), the down target by resting with eyes open (rest). A trial is a 1 s inter-stimulus interval then a 5 s target presentation; events.tsv onsets mark the target and the epoch covers those 5 s. Only the 74-channel EEG (recorded at 1 kHz with simultaneous MEG, both released downsampled to 250 Hz) is exposed. The six runs are the protocol’s testing phase (the paper also describes five feedback-free training runs per session used to calibrate the classifier); the resting-state recordings (task-rest) are not loaded.

The released events keep the authors’ checked trials (29-32 per run, 14,431 in total). The last rest cue of sub-03 ses-01 run-03 ends after the recording, so MotorImagery returns 14,430 epochs.

References

[1]

Corsi, M.-C., Gitton, C., Gonzalez-Astudillo, J., Kahn, A. E., Hugueville, L., Schwartz, D., George, N., Chavez, M., Dupont, S., Bassett, D. S., & De Vico Fallani, F. (2026). Understanding Brain-Computer Interfaces training: a longitudinal and multimodal dataset. Scientific Data. https://doi.org/10.1038/s41597-026-08237-5

Code Examples

from moabb.datasets import NETBCI2026
dataset = NETBCI2026()
data = dataset.get_data(subjects=[1])
print(data[1])

Metadata

Dataset summary

#Subj	19
#Chan	74
#Classes	2
#Trials / class	varies
Trials length	5 s
Freq	250 Hz
#Sessions	4
#Runs	6
Total_trials	14431

Participants

Population: healthy
Age: 27.47 (range: 19-35) years
Handedness: right-handed

Equipment

Amplifier: Easycap 74-channel passive Ag/AgCl EEG amplified by the MEGIN TRIUX MEG acquisition system (102 magnetometers, 204 gradiometers)
Montage: standard_1005
Reference: mastoids

Data Access

DOI: 10.1038/s41597-026-08237-5
Data URL: https://doi.org/10.57745/RBJRC7
Repository: Recherche Data Gouv

Experimental Protocol

Paradigm: imagery
Feedback: visual cursor
Stimulus: visual target

Notes

The events.tsv events replace the BrainVision markers (same two codes), so no trial is counted twice. Sixteen runs are not stored at 250 Hz (ten at 249.9 Hz, the six of sub-09 ses-02 at 1000 Hz); they are resampled to 250 Hz after the annotations (in seconds) are set.

The data (Recherche Data Gouv, doi:10.57745/RBJRC7, version 2.2) are one 49 GB zip64 archive of the whole BIDS tree plus small MD5-checked root files. The loader reads the archive’s central directory, then fetches only the subject’s EEG members, one HTTP range request each, and checks each member’s CRC-32 (about 386 MB per subject).

__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]#

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.

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/.