moabb.datasets.Wang2026#

class moabb.datasets.Wang2026(group='all', subjects=None, sessions=None, *, return_all_modalities=False)[source]#

Bases: BaseDataset

[source]

Dataset Snapshot

Wang2026

Imagery, 4 classes (left_hand vs right_hand vs hands vs rest)

AuthorsHanwen Wang, Yisha Zhang, Maxim Karrenbach, Yidan Ding, Bin He

🇺🇸 Carnegie Mellon University, US·2026·bhe1@andrew.cmu.edu

Imagery Code: Wang2026 39 subjects 5 sessions 62 ch 1000 Hz 4 classes 5.0 s trials CC BY-NC 4.0

Class Labels: left_hand, right_hand, hands, rest

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Overview

Sensory-guided joint-learning MI-BCI study from Wang et al. (2026).

The release contains 39 participants in four experimental groups: joint-learning (15), BCI2000 control (8), tactile control (8), and an independently recruited EEGNet control (8). group filters these conditions while preserving globally unique MOABB subject IDs.

The public files are trial-segmented MATLAB files, not original BCI2000 recordings, so MNE's BCI2000 reader does not apply. Fixed-length trials are cropped to five seconds. Variable-length trials shorter than five seconds are omitted rather than synthetically padded.

:param group: Experimental-group filter. :type group: {"all", "joint_learning", "bci2000_control", "tactile_control", "eegnet_control"} :param subjects: Optional global subject IDs within group. :type subjects: list of int | None :param sessions: Optional sessions to retain. :type sessions: list of int or str | None

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Citation & Impact

Paper DOI10.1038/s41467-026-75435-5
CitationsLoading…
Public APICrossref | OpenAlex
Data DOI10.1184/R1/32293995.v1

Stimulus Protocol

5s task window per trial · 4-class imagery paradigm · 1 runs/session across 5 sessions

HED Event Tags

HED tags4/4 events annotated

Source: MOABB BIDS HED annotation mapping.

Sensory-event

4

Agent-action

3

Experimental-stimulus

1

Rest

1

Visual-presentation

1

left_hand

Sensory-eventAgent-action

right_hand

Sensory-eventAgent-action

hands

Sensory-eventAgent-action

rest

Sensory-eventExperimental-stimulusVisual-presentationRest

HED tree view

Tree · left_hand

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

Tree · right_hand

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

Tree · hands

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

Tree · rest

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

Channel Summary

Total channels62

EEG62

Montage10-05

Sampling1000 Hz

Referencemidway between Cz and CPz

Filter{'notch': 60.0}

Notch / line60 Hz

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

Overview

Sensory-guided joint-learning MI-BCI study from Wang et al. (2026).

The release contains 39 participants in four experimental groups: joint-learning (15), BCI2000 control (8), tactile control (8), and an independently recruited EEGNet control (8). group filters these conditions while preserving globally unique MOABB subject IDs.

The public files are trial-segmented MATLAB files, not original BCI2000 recordings, so MNE’s BCI2000 reader does not apply. Fixed-length trials are cropped to five seconds. Variable-length trials shorter than five seconds are omitted rather than synthetically padded.

param group:: Experimental-group filter.
type group:: {“all”, “joint_learning”, “bci2000_control”, “tactile_control”, “eegnet_control”}
param subjects:: Optional global subject IDs within group.
type subjects:: list of int | None
param sessions:: Optional sessions to retain.
type sessions:: list of int or str | None

References

[1]

Wang, H., Zhang, Y., Karrenbach, M., Ding, Y., & He, B. (2026). Sensory-guided human-machine joint learning accelerates the acquisition of motor imagery brain computer interface control. Nature Communications, 17, 6177. https://doi.org/10.1038/s41467-026-75435-5

[2]

Wang, H., Zhang, Y., Karrenbach, M., Ding, Y., & He, B. (2026). EEG Datasets for Sensory-Guided Joint Learning in Motor Imagery Brain-Computer Interfaces. Carnegie Mellon University. https://doi.org/10.1184/R1/32293995.v1

Code Examples

from moabb.datasets import Wang2026
dataset = Wang2026()
data = dataset.get_data(subjects=[1])
print(data[1])

Metadata

Dataset summary

#Subj	39
#Chan	62
#Classes	4
#Trials / class	varies
Trials length	5 s
Freq	1000 Hz
#Sessions	5
#Runs	varies
Total_trials	varies

Participants

Population: healthy
BCI experience: naive

Equipment

Amplifier: Compumedics Neuroscan SynAmps 2/RT
Montage: standard_1005
Reference: midway between Cz and CPz

Preprocessing

Data state: trial-segmented
Steps: trial segmentation, auxiliary-channel exclusion, per-trial temporal centering in the MOABB reconstruction
Notes: The release contains 62 selected EEG channels and states that no additional filtering or downsampling was applied. Runs use either fixed-length EEGNet trials or variable-length BCI2000 trials.

Data Access

DOI: 10.1184/R1/32293995.v1
Data URL: https://kilthub.cmu.edu/articles/dataset/EEG_Datasets_for_Sensory-Guided_Joint_Learning_in_Motor_Imagery_Brain-Computer_Interfaces/32293995
Repository: KiltHub (Figshare)

Experimental Protocol

Paradigm: imagery
Task type: online 1D and 2D cursor control
Tasks: left hand imagery, right hand imagery, bilateral hand imagery, rest
Feedback: visual cursor feedback, with cohort-specific tactile guidance, EEGNet adaptation, or fixed BCI2000 decoding
Stimulus: visual cursor and directional cue

__init__(group='all', 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]#: Extract one subject without downloading its complete cohort archive.

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.

Parameters:

subject_list (list of int | None) – List of subjects id to download, if None all subjects are downloaded.
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.
accept (bool) – Accept licence term to download the data, if any. Default: False
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

property subject_mapping#: Map global IDs to (archive group, source subject).