NM000346: eeg dataset, 12 subjects#

CastillosCVEP100

Access recordings and metadata through EEGDash.

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

Modality: eeg Subjects: 12 Recordings: 12 License: CC-BY-4.0 Source: nemar

Metadata: Complete (100%)

Quickstart#

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)

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'])

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},
}

About This Dataset#

CastillosCVEP100

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

Dataset Overview

Code: CastillosCVEP100 Paradigm: cvep DOI: https://doi.org/10.1016/j.neuroimage.2023.120446

View full README

CastillosCVEP100

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

Dataset Overview

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

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

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) https://github.com/NeuroTechX/moabb

Dataset Information#

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
Copy-paste 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},
}

Found an issue with this dataset?

If you encounter any problems with this dataset (missing files, incorrect metadata, loading errors, etc.), please let us know!

Report an Issue on GitHub

Technical Details#

Subjects & recordings

  • Subjects: 12

  • Recordings: 12

  • Tasks: 1

Channels & sampling rate

  • Channels: 32

  • Sampling rate (Hz): 500.0

  • Duration (hours): 0.880271111111111

Tags

  • Pathology: Healthy

  • Modality: Visual

  • Type: Attention

Files & format

  • Size on disk: 150.6 MB

  • File count: 12

  • Format: BIDS

License & citation

  • License: CC-BY-4.0

  • DOI: doi:10.1016/j.neuroimage.2023.120446

Provenance

API Reference#

Use the NM000346 class to access this dataset programmatically.

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

Bases: EEGDashDataset

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, overwrite=False)[source]#

Save the dataset to disk.

Parameters:

  • path (str or Path) – Destination file path.

  • overwrite (bool, default False) – If True, overwrite existing file.

Return type:

None

See Also#