moabb.datasets.Schrag2026Pediatric#

class moabb.datasets.Schrag2026Pediatric(subjects=None, sessions=None, *, include_personalization=False)[source]#

Bases: BaseDataset

[source]

Dataset Snapshot

Schrag2026Pediatric

Open-access pediatric SSVEP-BCI dataset: 47 children aged 5-18 performing a personalization pipeline and an online 4-target SSVEP game with both personal and standard stimuli.

SSVEP, 4 classes (6.25 vs 10 vs 11.11 vs 14.28)

AuthorsEmily Schrag, Daniel Comaduran Marquez, Adam Kirton, Eli Kinney-Lang

🇨🇦 University of Calgary, CA·2026
SSVEP Code: Schrag2026Pediatric 47 subjects 1 session 16 ch 256 Hz 4 classes 5.0 s trials CC BY-ND 4.0

Class Labels: 6.25, 10, 11.11, 14.28

Read Paper Compare Similar Report Issue

Overview

SSVEP-based BCI dataset in children and adolescents (Schrag et al. 2026).

Dataset from, hosted on Zenodo

Forty-seven neurotypical children and adolescents (ages 5-18, mean 12.6 +/- 3.9 yr; 40.4% female) recorded with a g.tec g.GAMMAsys gel-based system (16 scalp channels at 256 Hz, ground Fpz, earlobe reference) completed a two-stage SSVEP-BCI session: (a) a personalization pipeline presenting 12 visual stimuli (4 contrasts x 3 sizes) all flickering at 10 Hz, and (b) an online 4-target SSVEP game at 6.25 / 10 / 11.11 / 14.28 Hz, played twice (once with the personal stimulus, once with a high-contrast standard) across two themed maps.

By default this class exposes the SSVEP game runs only -- a single session per subject holding two runs ("0standard", "1personal"), 5 s trials at four target frequencies. Set include_personalization=True to also load the 12-stimulus personalization recording as a third run "2personalization"; all its trials carry the "10" event since every personalization stimulus flickers at 10 Hz, conflating with the game's 10 Hz target if both are loaded.

Open in Overview tab →

Citation & Impact

Stimulus Protocol
../_images/Schrag2026Pediatric.svg

5s task window per trial · 4-class ssvep paradigm · 2 runs/session across 1 sessions

HED Event Tags
HED tags4/4 events annotated

Source: MOABB BIDS HED annotation mapping.

Experimental-stimulus
4
Label
4
Sensory-event
4
Visual-presentation
4
6.25
Sensory-eventExperimental-stimulusVisual-presentationLabel
10
Sensory-eventExperimental-stimulusVisual-presentationLabel
11.11
Sensory-eventExperimental-stimulusVisual-presentationLabel
14.28
Sensory-eventExperimental-stimulusVisual-presentationLabel

HED tree view

Tree · 6.25
├─ Sensory-event
├─ Experimental-stimulus
├─ Visual-presentation
└─ Label
Tree · 10
├─ Sensory-event
├─ Experimental-stimulus
├─ Visual-presentation
└─ Label
Tree · 11.11
├─ Sensory-event
├─ Experimental-stimulus
├─ Visual-presentation
└─ Label
Tree · 14.28
├─ Sensory-event
├─ Experimental-stimulus
├─ Visual-presentation
└─ Label
Channel Summary
Total channels16
EEG16 (active)
Montagestandard_1020
Sampling256 Hz
Referenceearlobe
Notch / line60 Hz

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

SSVEP-based BCI dataset in children and adolescents (Schrag et al. 2026).

Dataset from [1], hosted on Zenodo [2].

Forty-seven neurotypical children and adolescents (ages 5-18, mean 12.6 +/- 3.9 yr; 40.4% female) recorded with a g.tec g.GAMMAsys gel-based system (16 scalp channels at 256 Hz, ground Fpz, earlobe reference) completed a two-stage SSVEP-BCI session: (a) a personalization pipeline presenting 12 visual stimuli (4 contrasts x 3 sizes) all flickering at 10 Hz, and (b) an online 4-target SSVEP game at 6.25 / 10 / 11.11 / 14.28 Hz, played twice (once with the personal stimulus, once with a high-contrast standard) across two themed maps.

By default this class exposes the SSVEP game runs only – a single session per subject holding two runs ("0standard", "1personal"), 5 s trials at four target frequencies. Set include_personalization=True to also load the 12-stimulus personalization recording as a third run "2personalization"; all its trials carry the "10" event since every personalization stimulus flickers at 10 Hz, conflating with the game’s 10 Hz target if both are loaded.

Warning

Trial labels for the game runs come from the recorded fbCCA classifier output (the Selected SPO column of the per-game movement CSV) – the frequency the system identified during the live game, which then drove avatar movement. They are not ground-truth target frequencies; treating y as such biases benchmarks toward fbCCA’s behaviour. For ground-truth labels parse Intended Movement Direction together with the per-trial corner-to-frequency mapping (randomised across the game; not currently exposed by this loader).

Note

The dataset ships as a single ~1.2 GB DatasetData.zip on Zenodo. Subjects are extracted on demand; pyxdf is required (pip install moabb[xdf]).

References

[1]

E. Schrag, D. Comaduran Marquez, A. Kirton, and E. Kinney-Lang, “A steady-state visual evoked potential-based brain-computer interface dataset in children and adolescents,” Research Square preprint, 2026. DOI: 10.21203/rs.3.rs-9347306/v1

[2]

Schrag et al., 2026 SSVEP Pediatric Dataset. Zenodo. DOI: 10.5281/zenodo.19440997

from moabb.datasets import Schrag2026Pediatric
dataset = Schrag2026Pediatric()
data = dataset.get_data(subjects=[1])
print(data[1])

Dataset summary

#Subj

47

#Chan

16

#Classes

4

#Trials / class

3

Trials length

5 s

Freq

256 Hz

#Sessions

1

Participants

  • Population: healthy

  • Age: 12.6 (range: 5-18) years

  • BCI experience: naive

Equipment

  • Amplifier: g.tec g.GAMMAsys + g.USBamp + g.GAMMAcap

  • Electrodes: active

  • Montage: standard_1020

  • Reference: earlobe

Preprocessing

  • Data state: raw

Data Access

Experimental Protocol

  • Paradigm: ssvep

  • Task type: SSVEP-controlled videogame (4-target navigation)

  • Feedback: visual

  • Stimulus: flickering visual targets (4-target game)

__init__(subjects=None, sessions=None, *, include_personalization=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.

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)[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.

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