EEGdash›NeMAR›NM000346

Iss. 346 · 12 subjects · 12 recordings · CC-BY-4.0

Dataset Brief · CastillosCVEP100

NM000346: eeg dataset, 12 subjects#

CastillosCVEP100

Citation: Kalou Cabrera Castillos, Simon Ladouce, Ludovic Darmet, Frédéric Dehais (2023). CastillosCVEP100. 10.1016/j.neuroimage.2023.120446

12-participant EEG dataset — CastillosCVEP100.

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 NM000346

dataset = NM000346(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 = NM000346(cache_dir="./data", subject="01")

Advanced query

dataset = NM000346(
    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{nm000346,
  title = {CastillosCVEP100},
  author = {Kalou Cabrera Castillos and Simon Ladouce and Ludovic Darmet and Frédéric Dehais},
  doi = {10.1016/j.neuroimage.2023.120446},
  url = {https://doi.org/10.1016/j.neuroimage.2023.120446},
}

§ 02Study · The README

About This Dataset#

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

Code: CastillosCVEP100

Paradigm: cvep DOI: https://doi.org/10.1016/j.neuroimage.2023.120446 Subjects: 12 Sessions per subject: 1 Events: 0=100, 1=101 Trial interval: (0, 0.25) s File format: EEGLAB .set

CastillosCVEP100

Acquisition

Sampling rate: 500.0 Hz Number of channels: 32 Channel types: eeg=32 Channel names: C3, C4, CP1, CP2, CP5, CP6, Cz, F10, F3, F4, F7, F8, F9, FC1, FC2, FC5, FC6, Fp1, Fp2, Fz, O1, O2, Oz, P10, P3, P4, P7, P8, P9, Pz, T7, T8 Montage: standard_1020 Hardware: BrainProducts LiveAmp
View full README
CastillosCVEP100

Acquisition

Sampling rate: 500.0 Hz Number of channels: 32 Channel types: eeg=32 Channel names: C3, C4, CP1, CP2, CP5, CP6, Cz, F10, F3, F4, F7, F8, F9, FC1, FC2, FC5, FC6, Fp1, Fp2, Fz, O1, O2, Oz, P10, P3, P4, P7, P8, P9, Pz, T7, T8 Montage: standard_1020 Hardware: BrainProducts LiveAmp Reference: FCz Ground: FPz Sensor type: EEG Line frequency: 50.0 Hz Impedance threshold: 25.0 kOhm Cap manufacturer: BrainProducts Cap model: Acticap Electrode type: active

Participants

Number of subjects: 12 Health status: healthy Age: mean=30.6, std=7.1 Gender distribution: female=4, male=8 Species: human

Experimental Protocol

Paradigm: cvep Task type: visual attention Number of classes: 2 Class labels: 0, 1 Trial duration: 2.2 s Study design: factorial design (code type × amplitude depth) Study domain: BCI performance and user experience Feedback type: none Stimulus type: visual flashing Stimulus modalities: visual Primary modality: visual Synchronicity: synchronous Mode: offline Training/test split: False Instructions: focus on four targets that were cued sequentially in a random order for 0.5 s, followed by a 2.2 s stimulation phase, before a 0.7 s inter-trial period Stimulus presentation: display=Dell P2419HC LCD monitor, resolution=1920×1080 pixels, refresh_rate=60 Hz, brightness=265 cd/m², stimulus_size=150 pixels, background_luminance=124 lux (50% screen luminance), on_state_100=168 lux (100% amplitude depth), on_state_40=142 lux (40% amplitude depth), cue_duration=0.5 s, stimulation_duration=2.2 s, inter_trial_interval=0.7 s

HED Event Annotations

Schema: HED 8.4.0 | Browse: https://www.hedtags.org/hed-schema-browser 0
     ├─ 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: m-sequence (maximum-length sequence) Code length: 132 Number of targets: 4

Data Structure

Trials: 60 Blocks per session: 15 Trials context: 15 blocks × 4 trials (one per target) × 4 conditions (burst/mseq × 100%/40%)

Preprocessing

Data state: raw

Signal Processing

Classifiers: Convolutional Neural Network (CNN) Feature extraction: Sliding windows (250ms, 2ms stride), Standard deviation normalization Spatial filters: 16 spatial filters via 1D spatial convolution (8×1 kernel)

Cross-Validation

Method: sequential train/test split Evaluation type: offline classification

Performance (Original Study)

Accuracy: 85.0% Itr: 48.7 bits/min Selection Time S: 1.5 Cnn Training Time 6Blocks S: 40.0 Calibration Data 6Blocks S: 52.8

BCI Application

Applications: reactive BCI Environment: laboratory Online feedback: False

Tags

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

Documentation

Description: 4-class code-VEP BCI dataset comparing burst c-VEP and m-sequence stimulation at two amplitude depths (100% and 40%) to optimize performance and 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: Ethics committee of the University of Toulouse (CER approval number 2020-334); Declaration of Helsinki Keywords: Code-VEP, Reactive BCI, CNN, Amplitude depth reduction, Visual comfort

External Links

Source: https://zenodo.org/record/8255618 Github Code: neuroergoISAE/burst_codes Paper: https://doi.org/10.1016/j.neuroimage.2023.120446

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 an innovative variant of code-VEP, referred to as ‘Burst c-VEP’, involving the presentation of short bursts of aperiodic visual flashes at a deliberately slow rate, typically ranging from two to four flashes per second. The proposed solutions were tested through an offline 4-classes c-VEP protocol involving 12 participants. The full amplitude burst c-VEP sequences exhibited higher accuracy, ranging from 90.5% (with 17.6 s of calibration data) to 95.6% (with 52.8 s of calibration data), compared to its m-sequence counterpart (71.4% to 85.0%). The mean selection time for both types of codes (1.5 s) compared favorably to reports from previous studies. Lowering the intensity of the stimuli only slightly decreased the accuracy of the burst code sequences to 94.2% while leading to substantial improvements in terms of user experience.

Methodology

Factorial experimental design with 12 healthy participants. EEG recorded with BrainProducts LiveAmp 32-channel system at 500 Hz. Four conditions tested: burst c-VEP and m-sequence c-VEP, each at 100% and 40% amplitude depth. Participants focused on cued targets (4 classes) in 15 blocks of 4 trials per condition. CNN-based decoding with 250ms sliding windows. Subjective ratings collected for visual comfort, mental tiredness, and intrusiveness. VEP analysis included amplitude, latency, and inter-trial coherence metrics.

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-1 · 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.NM000346(cache_dir, query=None, s3_bucket=None, **kwargs)

Bases: EEGDashDataset

Author (year)Castillos2023_CastillosCVEP100

Canonical—

Importable asNM000346 · Castillos2023_CastillosCVEP100

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

class eegdash.dataset.NM000346(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#

CastillosCVEP100

Study:: nm000346 (NeMAR)
Author (year):: Castillos2023_CastillosCVEP100
Canonical:: —

Also importable as: NM000346, Castillos2023_CastillosCVEP100.

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/nm000346 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=nm000346 DOI: https://doi.org/10.1016/j.neuroimage.2023.120446

Examples

>>> from eegdash.dataset import NM000346
>>> dataset = NM000346(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 — NM000346.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 nm000346 to reproduce the tutorial on this dataset.

Citation

Kalou Cabrera Castillos, Simon Ladouce, Ludovic Darmet, Frédéric Dehais (2023). CastillosCVEP100. 10.1016/j.neuroimage.2023.120446

Provenance

¹Contributed to nemar in BIDS format.

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

³Persistent identifier: 10.1016/j.neuroimage.2023.120446.

Related & sibling datasets

NM000163EEG · 12 subjSame authors NM000344EEG · 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 · 10.1016/j.neuroimage.2023.120446

Machine-readable

schema.org/Dataset · Croissant

Mirrors

OpenNeuro · NEMAR · HF org

Dataset ID	`NM000346`
Title	CastillosCVEP100
Author (year)	`Castillos2023_CastillosCVEP100`
Canonical	—
Importable as	`NM000346`, `Castillos2023_CastillosCVEP100`
Year	2023
Authors	Kalou Cabrera Castillos, Simon Ladouce, Ludovic Darmet, Frédéric Dehais
License	CC-BY-4.0
Citation / DOI	doi:10.1016/j.neuroimage.2023.120446
Source links	OpenNeuro \| NeMAR \| Source URL

NM000346: 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#