EEGdash›NeMAR›NM000163

Iss. 163 · 12 subjects · 12 recordings · CC-BY-4.0

Dataset Brief · c-VEP and Burst-VEP dataset from Castillos et al. (2023)

NM000163: eeg dataset, 12 subjects#

c-VEP and Burst-VEP dataset from Castillos et al. (2023)

Citation: Kalou Cabrera Castillos, Simon Ladouce, Ludovic Darmet, Frédéric Dehais (2023). c-VEP and Burst-VEP dataset from Castillos et al. (2023). 10.82901/nemar.nm000163

12-participant EEG dataset — c-VEP and Burst-VEP dataset from Castillos et al. (2023).

Data & curation Kalou Cabrera Castillos · Simon Ladouce · Ludovic Darmet · Frédéric Dehais
Year 2023 · Distributed via NeMAR
Funding AID (Powerbrain project), France · AXA Research Fund Chair for Neuroergonomics, France · + 1 more

EEG · 32 ch500 HzBIDS 1.9.0Task · cvepHealthyVisualAttention

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 NM000163

dataset = NM000163(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 = NM000163(cache_dir="./data", subject="01")

Advanced query

dataset = NM000163(
    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{nm000163,
  title = {c-VEP and Burst-VEP dataset from Castillos et al. (2023)},
  author = {Kalou Cabrera Castillos and Simon Ladouce and Ludovic Darmet and Frédéric Dehais},
  doi = {10.82901/nemar.nm000163},
  url = {https://doi.org/10.82901/nemar.nm000163},
}

§ 02Study · The README

About This Dataset#

c-VEP and Burst-VEP dataset from Castillos et al. (2023)

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

DOI

c-VEP and Burst-VEP dataset from Castillos et al. (2023)

View full README

DOI

c-VEP and Burst-VEP dataset from Castillos et al. (2023)

     ├─ Sensory-event
     ├─ Experimental-stimulus
     ├─ Visual-presentation
     └─ Label/intensity_0

1

├─ Sensory-event
├─ Experimental-stimulus
├─ Visual-presentation
└─ Label/intensity_1

Paradigm-Specific Parameters

Detected paradigm: cvep
Code type: burst
Number of targets: 4
Cue duration: 0.5 s

Data Structure

Trials: 60
Blocks per session: 15
Trials context: 15 blocks x 4 trials per block = 60 trials per subject for burst c-VEP at 100% amplitude

Preprocessing

Data state: raw

Signal Processing

Classifiers: Convolutional Neural Network (CNN), Pearson correlation
Feature extraction: CNN spatial filtering (8x1 kernel, 16 filters), CNN temporal filtering (1x32 kernel with dilation 2, 8 filters), CNN 2D convolution (5x5 kernel, 4 filters), sliding windows (250ms, 2ms stride)
Frequency bands: analyzed=[0.1, 40.0] Hz
Spatial filters: CNN 8x1 spatial convolution (16 filters)

Cross-Validation

Method: sequential train/test split
Evaluation type: offline classification, iterative calibration (1-6 blocks)

Performance (Original Study)

Accuracy: 95.6%
Itr: 67.49 bits/min
Selection Time S: 1.5
Cnn Training Time S: 15.0
Burst 40 Accuracy: 94.2
Mseq 100 Accuracy: 85.0

BCI Application

Applications: reactive BCI
Environment: controlled laboratory
Online feedback: False

Tags

Pathology: Healthy
Modality: EEG
Type: reactive BCI, c-VEP, visual evoked potentials

Documentation

Description: Burst c-VEP based BCI study comparing novel burst code sequences to traditional m-sequences at two amplitude depths (100% and 40%) to optimize classification performance, minimize calibration data, and improve user experience
DOI: 10.1016/j.neuroimage.2023.120446
Associated paper DOI: 10.1016/j.neuroimage.2023.120446
License: CC-BY-4.0
Investigators: Kalou Cabrera Castillos, Simon Ladouce, Ludovic Darmet, Frédéric Dehais
Senior author: Frédéric Dehais
Contact: kalou.cabrera-castillos@isae-supaero.fr
Institution: Institut Supérieur de l’Aéronautique et de l’Espace (ISAE-SUPAERO)
Department: Human Factors and Neuroergonomics
Address: 10 Av. Edouard Belin, Toulouse, 31400, France
Country: FR
Repository: Zenodo
Data URL: https://zenodo.org/record/8255618
Publication year: 2023
Funding: AID (Powerbrain project), France; AXA Research Fund Chair for Neuroergonomics, France; Chair for Neuroadaptive Technology, Artificial and Natural Intelligence Toulouse Institute (ANITI), France
Ethics approval: University of Toulouse ethics committee (CER approval number 2020-334); Declaration of Helsinki
Acknowledgements: This work was funded by AID (Powerbrain project), France, the AXA Research Fund Chair for Neuroergonomics, France and Chair for Neuroadaptive Technology, Artificial and Natural Intelligence Toulouse Institute (ANITI), France.
Keywords: Code-VEP, Reactive BCI, CNN, Amplitude depth reduction, Visual comfort

External Links

Source: https://zenodo.org/record/8255618
Github: neuroergoISAE/burst_codes

Abstract

The utilization of aperiodic flickering visual stimuli under the form of code-modulated Visual Evoked Potentials (c-VEP) represents a pivotal advancement in the field of reactive Brain–Computer Interface (rBCI). This study introduces Burst c-VEP, an innovative variant involving short bursts of aperiodic visual flashes at 2-4 flashes per second. The proposed burst c-VEP sequences exhibited higher accuracy (90.5%-95.6%) compared to m-sequence counterparts (71.4%-85.0%) with mean selection time of 1.5s. Reducing stimulus intensity to 40% amplitude depth only slightly decreased accuracy to 94.2% while substantially improving user experience. The collected dataset and CNN architecture implementation are shared through open-access repositories.

Methodology

Twelve healthy participants completed an offline 4-class c-VEP protocol using a factorial design. EEG was recorded at 500 Hz using BrainProducts LiveAmp 32-channel system. Participants focused on cued targets with factorial manipulation of pattern type (burst vs m-sequence) and amplitude depth (100% vs 40%). Visual stimuli were presented on a 60 Hz Dell monitor. Burst codes consisted of brief flashes (~50ms) with minimum 200ms inter-burst interval, while m-sequences used Fibonacci-type LFSR with segmented 132-frame subsequences. A CNN architecture with spatial (8x1, 16 filters), temporal (1x32, 8 filters), and 2D convolution (5x5, 4 filters) layers decoded EEG using 250ms sliding windows with 2ms stride. Calibration data ranged from 1-6 blocks (8.8-52.8s). Classification used sequential train/test splits with Pearson correlation for target selection. VEP analysis examined amplitude, latency, and inter-trial coherence. Statistical analyses used 2×2 repeated measures ANOVA.

References

Kalou Cabrera Castillos. (2023). 4-class code-VEP EEG data [Data set]. Zenodo.(dataset). DOI: https://doi.org/10.5281/zenodo.8255618 Kalou Cabrera Castillos, Simon Ladouce, Ludovic Darmet, Frédéric Dehais. Burst c-VEP Based BCI: Optimizing stimulus design for enhanced classification with minimal calibration data and improved user experience,NeuroImage,Volume 284, 2023,120446,ISSN 1053-8119 DOI: https://doi.org/10.1016/j.neuroimage.2023.120446 Notes .. versionadded:: 1.1.0 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 1.5.0 (Mother of All BCI Benchmarks) NeuroTechX/moabb

§ 03Cohort · Participants

Cohort#

Dataset Statistics#

Age distribution by gender (n=12, range 31–31 yr, mean 30.0 yr)

30

Other · 12

Channel counts: 32 ch (n=12 recordings)

Sampling frequencies: 500.0 Hz (n=12 recordings)

Total recording duration: 52 min

§ 04Signal · Electrodes & trace

Signal · Electrodes & live trace#

Fig. 01 Signal & montage 32 ch · EEG · 500 Hz · 12 subjects, 12 recordings

Live trace viewer — sub-12 · ses-0 · task-cvep · run-0

Showing one representative recording out of 12 subjects and 12 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 · 32 sensors — 32 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.NM000163(cache_dir, query=None, s3_bucket=None, **kwargs)

Bases: EEGDashDataset

Author (year)Castillos2023_VEP

Canonical—

Importable asNM000163 · Castillos2023_VEP

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

class eegdash.dataset.NM000163(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#

c-VEP and Burst-VEP dataset from Castillos et al. (2023)

Study:: nm000163 (NeMAR)
Author (year):: Castillos2023_VEP
Canonical:: —

Also importable as: NM000163, Castillos2023_VEP.

Modality: eeg; Experiment type: Attention; Subject type: Healthy. Subjects: 12; recordings: 12; 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/nm000163 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=nm000163 DOI: https://doi.org/10.82901/nemar.nm000163

Examples

>>> from eegdash.dataset import NM000163
>>> dataset = NM000163(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 — NM000163.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 nm000163 to reproduce the tutorial on this dataset.

Citation

Kalou Cabrera Castillos, Simon Ladouce, Ludovic Darmet, Frédéric Dehais (2023). c-VEP and Burst-VEP dataset from Castillos et al. (2023). 10.82901/nemar.nm000163

Provenance

¹Contributed to nemar in BIDS format.

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

³Persistent identifier: 10.82901/nemar.nm000163.

Related & sibling datasets

NM000344EEG · 12 subjSame authors NM000346EEG · 12 subjSame authors NM000342EEG · 12 subjSame authors NM000345EEG · 12 subjSame authors NM000206EEG · 15 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.nm000163

Machine-readable

schema.org/Dataset · Croissant

Mirrors

OpenNeuro · NEMAR · HF org

Dataset ID	`NM000163`
Title	c-VEP and Burst-VEP dataset from Castillos et al. (2023)
Author (year)	`Castillos2023_VEP`
Canonical	—
Importable as	`NM000163`, `Castillos2023_VEP`
Year	2023
Authors	Kalou Cabrera Castillos, Simon Ladouce, Ludovic Darmet, Frédéric Dehais
License	CC-BY-4.0
Citation / DOI	10.82901/nemar.nm000163
Source links	OpenNeuro \| NeMAR \| Source URL

NM000163: eeg dataset, 12 subjects#

Quickstart#

About This Dataset#

Cohort#

Dataset Statistics#

Signal · Electrodes & live trace#

NEMAR Processing Statistics#

Manifest#

File Explorer#

API Reference#

Citation

Provenance

Related & sibling datasets

See Also#