EEGdash›NeMAR›NM000334

Iss. 334 · 41 subjects · 76 recordings · CC-BY-4.0

Dataset Brief · Schrag2026Pediatric

NM000334: eeg dataset, 41 subjects#

Schrag2026Pediatric: pediatric SSVEP-BCI dataset in children and adolescents

Access recordings and metadata through EEGDash.

Citation: Emily Schrag, Daniel Comaduran Marquez, Adam Kirton, Eli Kinney-Lang (2019). Schrag2026Pediatric: pediatric SSVEP-BCI dataset in children and adolescents. 10.82901/nemar.nm000334

Modality: eeg Subjects: 41 Recordings: 76 License: CC-BY-4.0 Source: nemar

Metadata: Complete (100%)

41-participant EEG dataset — Schrag2026Pediatric: pediatric SSVEP-BCI dataset in children and adolescents.

Data & curation Emily Schrag · Daniel Comaduran Marquez · Adam Kirton · Eli Kinney-Lang
Senior author Eli Kinney-Lang
Year 2019 · Distributed via NeMAR

EEG · 16 ch256 HzBIDS 1.9.0Task · ssvep

Layer 01Study

What was asked

Hypothesis, independent & dependent variables, paradigm, cohort, and the editorial caveats around what the recordings can and cannot answer.

Layer 02Signal · BIDS

What was recorded

Sidecars, channels & electrodes, coordinate system, event semantics, and quality stats from the NEMAR pipeline when available.

Layer 03Training · ML

What you can train on

Recommended access modes — MNE Raw, braindecode windows, PyTorch DataLoader — plus the targets the metadata makes addressable.

§ 01Access · Get started

Quickstart#

Get Started

Install

pip install eegdash

Access the data

from eegdash.dataset import NM000334

dataset = NM000334(cache_dir="./data")
# Get the raw object of the first recording
raw = dataset.datasets[0].raw
print(raw.info)

Query & Filter

Filter by subject

dataset = NM000334(cache_dir="./data", subject="01")

Advanced query

dataset = NM000334(
    cache_dir="./data",
    query={"subject": {"$in": ["01", "02"]}},
)

Iterate recordings

for rec in dataset:
    print(rec.subject, rec.raw.info['sfreq'])

Cite This Dataset

If you use this dataset in your research, please cite the original authors.

BibTeX

@dataset{nm000334,
  title = {Schrag2026Pediatric: pediatric SSVEP-BCI dataset in children and adolescents},
  author = {Emily Schrag and Daniel Comaduran Marquez and Adam Kirton and Eli Kinney-Lang},
  doi = {10.82901/nemar.nm000334},
  url = {https://doi.org/10.82901/nemar.nm000334},
}

§ 02Study · The README

About This Dataset#

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

Code: Schrag2026Pediatric

Paradigm: ssvep DOI: 10.21203/rs.3.rs-9347306/v1 Subjects: 47 Sessions per subject: 1 Events: 6.25=1, 10=2, 11.11=3, 14.28=4 Trial interval: [0.0, 5.0] s Runs per session: 2 File format: XDF

DOI

Schrag2026Pediatric

Acquisition

Sampling rate: 256.0 Hz Number of channels: 16 Channel types: eeg=16 Channel names: Fz, F4, F8, C3, Cz, C4, T8, P7, P3, P4, P8, PO7, PO8, O1, Oz, O2

View full README

DOI

Schrag2026Pediatric

Acquisition

Sampling rate: 256.0 Hz Number of channels: 16 Channel types: eeg=16 Channel names: Fz, F4, F8, C3, Cz, C4, T8, P7, P3, P4, P8, PO7, PO8, O1, Oz, O2 Montage: standard_1020 Hardware: g.tec g.GAMMAsys + g.USBamp + g.GAMMAcap Software: Unity3D + BCI-Essentials Reference: earlobe Ground: Fpz Sensor type: active Line frequency: 60.0 Hz Cap manufacturer: g.tec Electrode type: wet Electrode material: Ag/AgCl gel

Participants

Number of subjects: 47 Health status: healthy Age: mean=12.6, std=3.9, min=5, max=18 Gender distribution: female=19, male=28 BCI experience: naive Species: human

Experimental Protocol

Paradigm: ssvep Task type: SSVEP-controlled videogame (4-target navigation) Number of classes: 4 Class labels: 6.25, 10, 11.11, 14.28 Trial duration: 5.0 s Study design: Per-subject pipeline: (1) personalization (12 stimuli at 10 Hz, 5 s on / 5 s baseline / pairwise comfort, ~20 sets), (2) online 4-target SSVEP game played twice – personal stimulus and standard high-contrast stimulus across two themed maps. Feedback type: visual Stimulus type: flickering visual targets (4-target game) Stimulus modalities: visual Primary modality: visual Synchronicity: synchronous Mode: online

HED Event Annotations

Schema: HED 8.4.0 | Browse: https://www.hedtags.org/hed-schema-browser 6.25

     ├─ Sensory-event
     ├─ Experimental-stimulus
     ├─ Visual-presentation
     └─ Label/6_25

10

     ├─ Sensory-event
     ├─ Experimental-stimulus
     ├─ Visual-presentation
     └─ Label/10

11.11

     ├─ Sensory-event
     ├─ Experimental-stimulus
     ├─ Visual-presentation
     └─ Label/11_11

14.28

├─ Sensory-event
├─ Experimental-stimulus
├─ Visual-presentation
└─ Label/14_28

Paradigm-Specific Parameters

Detected paradigm: ssvep Stimulus frequencies: [6.25, 10.0, 11.11, 14.28] Hz

Data Structure

Trials: 12 Trials context: Each game session contains ~30-160 movement trials (one per 5 s SSVEP stimulation period). Of these, exactly 12 are ground-truth target events (4 frequencies x 3 predefined target positions, minus skipped events on certain map layouts; see Notes.pdf in the Zenodo deposit). The remaining trials are user-driven movements whose labels are fbCCA classifier outputs, not ground truth – this loader exposes all classifier-labelled trials with non-empty Selected SPO.

Preprocessing

Data state: raw Preprocessing applied: False

Signal Processing

Classifiers: fbCCA Feature extraction: fbCCA Frequency bands: analysis=[3.0, 29.0] Hz

BCI Application

Applications: navigation game Environment: lab Online feedback: True

Tags

Pathology: healthy Modality: visual Type: perception

Documentation

Description: 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. DOI: 10.5281/zenodo.20848097 Associated paper DOI: 10.21203/rs.3.rs-9347306/v1 License: CC-BY-4.0 Investigators: Emily Schrag, Daniel Comaduran Marquez, Adam Kirton, Eli Kinney-Lang Senior author: Eli Kinney-Lang Institution: University of Calgary Country: CA Repository: Zenodo Data URL: https://doi.org/10.5281/zenodo.20848097 Publication year: 2026 Ethics approval: University of Calgary Conjoint Health Research Ethics Board, REB25-0723 Keywords: SSVEP, BCI, pediatric, children, adolescents, stimulus personalization, comfort, EEG

Ethics

Approved by the University of Calgary’s Conjoint Health Research Ethics Board under ID REB25-0723. Informed assent and parental consent were obtained for all participants, and participants/guardians explicitly consented to the sharing of their de-identified data (Schrag et al. 2026, 10.21203/rs.3.rs-9347306/v1).

References

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 Schrag et al., 2026 SSVEP Pediatric Dataset. Zenodo. DOI: 10.5281/zenodo.19440997 Appelhoff, S., Sanderson, M., Brooks, T., Vliet, M., Quentin, R., Holdgraf, C., Chaumon, M., Mikulan, E., Tavabi, K., Hochenberger, R., Welke, D., Brunner, C., Rockhill, A., Larson, E., Gramfort, A. and Jas, M. (2019). MNE-BIDS: Organizing electrophysiological data into the BIDS format and facilitating their analysis. Journal of Open Source Software 4: (1896). https://doi.org/10.21105/joss.01896 Pernet, C. R., Appelhoff, S., Gorgolewski, K. J., Flandin, G., Phillips, C., Delorme, A., Oostenveld, R. (2019). EEG-BIDS, an extension to the brain imaging data structure for electroencephalography. Scientific Data, 6, 103. https://doi.org/10.1038/s41597-019-0104-8 Generated by MOABB 1.8.0dev0 (Mother of All BCI Benchmarks) NeuroTechX/moabb

NEMAR Metadata#

[![DOI](https://img.shields.io/badge/DOI-10.82901%2Fnemar.nm000334-blue)](https://doi.org/10.82901/nemar.nm000334) Schrag2026Pediatric =================== SSVEP-based BCI dataset in children and adolescents (Schrag et al. 2026). Dataset Overview —————-

Code: Schrag2026Pediatric Paradigm: ssvep DOI: 10.21203/rs.3.rs-9347306/v1 Subjects: 47 Sessions per subject: 1 Events: 6.25=1, 10=2, 11.11=3, 14.28=4 Trial interval: [0.0, 5.0] s Runs per session: 2 File format: XDF

Acquisition#

Sampling rate: 256.0 Hz Number of channels: 16 Channel types: eeg=16 Channel names: Fz, F4, F8, C3, Cz, C4, T8, P7, P3, P4, P8, PO7, PO8, O1, Oz, O2 Montage: standard_1020 Hardware: g.tec g.GAMMAsys + g.USBamp + g.GAMMAcap Software: Unity3D + BCI-Essentials Reference: earlobe Ground: Fpz Sensor type: active Line frequency: 60.0 Hz Cap manufacturer: g.tec Electrode type: wet Electrode material: Ag/AgCl gel

Participants#

Number of subjects: 47 Health status: healthy Age: mean=12.6, std=3.9, min=5, max=18 Gender distribution: female=19, male=28 BCI experience: naive Species: human

Experimental Protocol#

Paradigm: ssvep Task type: SSVEP-controlled videogame (4-target navigation) Number of classes: 4 Class labels: 6.25, 10, 11.11, 14.28 Trial duration: 5.0 s Study design: Per-subject pipeline: (1) personalization (12 stimuli at 10 Hz, 5 s on / 5 s baseline / pairwise comfort, ~20 sets), (2) online 4-target SSVEP game played twice – personal stimulus and standard high-contrast stimulus across two themed maps. Feedback type: visual Stimulus type: flickering visual targets (4-target game) Stimulus modalities: visual Primary modality: visual Synchronicity: synchronous Mode: online

HED Event Annotations#

Schema: HED 8.4.0 | Browse: https://www.hedtags.org/hed-schema-browser 6.25

├─ Sensory-event ├─ Experimental-stimulus ├─ Visual-presentation └─ Label/6_25

10
├─ Sensory-event ├─ Experimental-stimulus ├─ Visual-presentation └─ Label/10

11.11
├─ Sensory-event ├─ Experimental-stimulus ├─ Visual-presentation └─ Label/11_11

14.28
├─ Sensory-event ├─ Experimental-stimulus ├─ Visual-presentation └─ Label/14_28

Paradigm-Specific Parameters#

Detected paradigm: ssvep Stimulus frequencies: [6.25, 10.0, 11.11, 14.28] Hz

Data Structure#

Trials: 12 Trials context: Each game session contains ~30-160 movement trials (one per 5 s SSVEP stimulation period). Of these, exactly 12 are ground-truth target events (4 frequencies x 3 predefined target positions, minus skipped events on certain map layouts; see Notes.pdf in the Zenodo deposit). The remaining trials are user-driven movements whose labels are fbCCA classifier outputs, not ground truth – this loader exposes all classifier-labelled trials with non-empty Selected SPO.

Preprocessing#

Data state: raw Preprocessing applied: False

Signal Processing#

Classifiers: fbCCA Feature extraction: fbCCA Frequency bands: analysis=[3.0, 29.0] Hz

BCI Application#

Applications: navigation game Environment: lab Online feedback: True

Tags#

Pathology: healthy Modality: visual Type: perception

Documentation#

Description: 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. DOI: 10.5281/zenodo.20848097 Associated paper DOI: 10.21203/rs.3.rs-9347306/v1 License: CC-BY-4.0 Investigators: Emily Schrag, Daniel Comaduran Marquez, Adam Kirton, Eli Kinney-Lang Senior author: Eli Kinney-Lang Institution: University of Calgary Country: CA Repository: Zenodo Data URL: https://doi.org/10.5281/zenodo.20848097 Publication year: 2026 Ethics approval: University of Calgary Conjoint Health Research Ethics Board, REB25-0723 Keywords: SSVEP, BCI, pediatric, children, adolescents, stimulus personalization, comfort, EEG

Ethics#

Approved by the University of Calgary’s Conjoint Health Research Ethics Board under ID REB25-0723. Informed assent and parental consent were obtained for all participants, and participants/guardians explicitly consented to the sharing of their de-identified data (Schrag et al. 2026, 10.21203/rs.3.rs-9347306/v1).

References#

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 Schrag et al., 2026 SSVEP Pediatric Dataset. Zenodo. DOI: 10.5281/zenodo.19440997 Appelhoff, S., Sanderson, M., Brooks, T., Vliet, M., Quentin, R., Holdgraf, C., Chaumon, M., Mikulan, E., Tavabi, K., Hochenberger, R., Welke, D., Brunner, C., Rockhill, A., Larson, E., Gramfort, A. and Jas, M. (2019). MNE-BIDS: Organizing electrophysiological data into the BIDS format and facilitating their analysis. Journal of Open Source Software 4: (1896). https://doi.org/10.21105/joss.01896 Pernet, C. R., Appelhoff, S., Gorgolewski, K. J., Flandin, G., Phillips, C., Delorme, A., Oostenveld, R. (2019). EEG-BIDS, an extension to the brain imaging data structure for electroencephalography. Scientific Data, 6, 103. https://doi.org/10.1038/s41597-019-0104-8 — Generated by MOABB 1.8.0dev0 (Mother of All BCI Benchmarks) NeuroTechX/moabb

License: CC-BY-4.0

Authors:

Emily Schrag
Daniel Comaduran Marquez
Adam Kirton
Eli Kinney-Lang

Versions:

Version	DOI	Released
`current`	10.82901/nemar.nm000334

§ 03Cohort · Participants

Cohort#

Dataset Statistics#

Age distribution by gender (n=41, range 6–18 yr, mean 13.2 yr)

51015

Female · 16Male · 25

Sex composition

Female
16
Male
25
F : M ratio
0.64 : 1

39% female · n = 41 subjects with reported sex.

Channel counts: 16 ch (n=76 recordings)

Sampling frequencies: 256.0 Hz (n=76 recordings)

Total recording duration: 9 h 0 min

§ 04Signal · Electrodes & trace

Signal · Electrodes & live trace#

Fig. 01 Signal & montage 16 ch · EEG · 256 Hz · 41 subjects, 76 recordings

Live trace viewer — sub-47 · ses-0 · task-ssvep · run-1

Showing one representative recording out of 41 subjects and 76 recordings in this dataset. Browse the full set on OpenNeuro; drop any other _eeg.{set,edf,bdf,vhdr} file onto the viewer (or pass ?eeg=<url>) to inspect it.

Electrode layout — EEG · 16 sensors — 16 channels

NEMAR Processing Statistics#

The plots below are generated by NEMAR’s automated EEG pipeline. The histogram shows pipeline success for data cleaning and ICA decomposition, the percentage of data frames and EEG channels retained after artefact removal, line noise per channel (RMS, dB), and the age/gender distribution of participants.

HED event descriptors word cloud

§ 05Manifest · BIDS tree

Manifest#

File Explorer#

Browse the BIDS file structure of this dataset. Records are fetched on demand from the EEGDash catalog the first time you open the explorer.

Recordings—

Files—

Subjects—

Modalities—

Click to load file structure…

§ 06API · Programmatic access

API Reference#

Signature

eegdash.dataset

class

eegdash.dataset.NM000334(cache_dir, query=None, s3_bucket=None, **kwargs)

Bases: EEGDashDataset

Author (year)—

Canonical—

Importable asNM000334

Sourceeegdash/dataset/registry.py · [source ↗]

class eegdash.dataset.NM000334(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#

Schrag2026Pediatric: pediatric SSVEP-BCI dataset in children and adolescents

Study:: nm000334 (NeMAR)
Author (year):: —
Canonical:: —

Also importable as: NM000334.

Modality: eeg; Subject type: Unknown. Subjects: 41; recordings: 76; tasks: 1.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir#

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query#

Merged query with the dataset filter applied.

Type:: dict

records#

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/nm000334 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=nm000334 DOI: https://doi.org/10.82901/nemar.nm000334

Examples

>>> from eegdash.dataset import NM000334
>>> dataset = NM000334(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

__init__(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#

save(path: str, overwrite: bool = False, offset: int = 0)[source]#

Save datasets to files by creating one subdirectory for each dataset:

path/
    0/
        0-raw.fif | 0-epo.fif
        description.json
        raw_preproc_kwargs.json (if raws were preprocessed)
        window_kwargs.json (if this is a windowed dataset)
        window_preproc_kwargs.json  (if windows were preprocessed)
        target_name.json (if target_name is not None and dataset is raw)
    1/
        1-raw.fif | 1-epo.fif
        description.json
        raw_preproc_kwargs.json (if raws were preprocessed)
        window_kwargs.json (if this is a windowed dataset)
        window_preproc_kwargs.json  (if windows were preprocessed)
        target_name.json (if target_name is not None and dataset is raw)

Parameters:

path (str) –

Directory in which subdirectories are created to store
-raw.fif | -epo.fif and .json files to.
overwrite (bool) – Whether to delete old subdirectories that will be saved to in this call.
offset (int) – If provided, the integer is added to the id of the dataset in the concat. This is useful in the setting of very large datasets, where one dataset has to be processed and saved at a time to account for its original position.

Access modesMNE → braindecode → PyTorch → ML

.rawMNE Raw object — standard tools (filter, epoch, ICA, plot_psd).mne

BaseConcatDatasetEach record is a lazy BaseDataset from braindecode — windowed via create_windows_from_events.braindecode

DataLoaderWraps the windowed dataset into a PyTorch DataLoader; supports parallel workers and on-the-fly augmentations.pytorch

Zarr cacheOptional braindecode Zarr mirror for fast resume; persisted to cache_dir.zarr

Hugging FaceNo per-dataset mirror published yet — browse the EEGDash org listing for sibling datasets. See the datasets loader API.huggingface

Croissant 1.0Machine-readable JSON-LD descriptor — NM000334.croissant.json (MLCommons schema, ingestible by PyTorch / TensorFlow / JAX).mlcommons

Examples using EEGDashcurated · start here

Find datasets with the EEGDash APIQuery the catalogue, filter by task or modality, list candidates.

Load one EEG recordingResolve a single record to an MNE Raw with channels and events.

EEG recording to PyTorch DataLoaderWrap braindecode windows in a DataLoader for model training.

Preprocess EEG and create windowsFilter, resample, epoch — and persist the windowed dataset.

Save and reload prepared dataCache a windowed dataset to disk and reattach it without recompute.

Download a dataset locallyPrefetch BIDS files to a local cache and validate the layout.

Swap any load_dataset(...) call for nm000334 to reproduce the tutorial on this dataset.

Citation

Emily Schrag, Daniel Comaduran Marquez, Adam Kirton, Eli Kinney-Lang (2019). Schrag2026Pediatric: pediatric SSVEP-BCI dataset in children and adolescents. 10.82901/nemar.nm000334

Provenance

¹Contributed to nemar in BIDS format.

²Curated & ingested by the EEGDash catalog; see CITATION.cff for canonical reference.

³Persistent identifier: 10.82901/nemar.nm000334.

Related & sibling datasets

NM000280EEG · 47 subj NM000327EEG · 55 subj NM000328EEG · 60 subj NM000324EEG · 32 subj NM000291EEG · 14 subj

+ 1 more — see See Also below →

BIDS

BIDS 1.9.0

Sidecars

events · events.json · channels · eeg.json

Provenance

CC-BY-4.0 · 10.82901/nemar.nm000334

Machine-readable

schema.org/Dataset · Croissant

Mirrors

OpenNeuro · NEMAR · HF org

Dataset ID	`NM000334`
Title	Schrag2026Pediatric: pediatric SSVEP-BCI dataset in children and adolescents
Author (year)	—
Canonical	—
Importable as	`NM000334`
Year	2019
Authors	Emily Schrag, Daniel Comaduran Marquez, Adam Kirton, Eli Kinney-Lang
License	CC-BY-4.0
Citation / DOI	10.82901/nemar.nm000334
Source links	OpenNeuro \| NeMAR \| Source URL

NM000334: eeg dataset, 41 subjects#

Quickstart#

About This Dataset#

NEMAR Metadata#

Acquisition#

Participants#

Experimental Protocol#

HED Event Annotations#

Paradigm-Specific Parameters#

Data Structure#

Preprocessing#

Signal Processing#

BCI Application#

Tags#

Documentation#

Ethics#

References#

Cohort#

Dataset Statistics#

Signal · Electrodes & live trace#

NEMAR Processing Statistics#

Manifest#

File Explorer#

API Reference#

Citation

Provenance

Related & sibling datasets

See Also#