moabb.datasets.CastillosCVEP40#

class moabb.datasets.CastillosCVEP40(window_size=0.25, subjects=None, sessions=None, **kwargs)[source]#

Bases: BaseCastillos2023

[source]

Dataset Snapshot

CastillosCVEP40

Burst c-VEP Based BCI: Optimizing stimulus design for enhanced classification with minimal calibration data and improved user experience

c-VEP, 2 classes (0 vs 1)

AuthorsKalou Cabrera Castillos, Simon Ladouce, Ludovic Darmet, Frédéric Dehais

🇫🇷 Institut Supérieur de l'Aéronautique et de l'Espace (ISAE-SUPAERO), FR·2023·kalou.cabrera-castillos@isae-supaero.fr
c-VEP Code: CastillosCVEP40 12 subjects 1 session 32 ch 500 Hz 2 classes 2.2 s trials CC BY 4.0

Class Labels: 0, 1

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Overview

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

Dataset from the study on burst-VEP

Dataset description

Participants were comfortably seated and instructed to read and sign the informed consent. EEG data were recorded using a BrainProduct LiveAmp 32 active electrodes wet-EEG setup with a sample rate of 500 Hz to record the surface brain activity. The 32 electrodes were placed following the 10–20 international system on a BrainProduct Acticap. The ground electrode was placed at the FPz electrode location and all electrodes were referenced to the FCz electrode. The impedance of all electrodes was brought below 25kOhm prior to recording onset. Once equipped with the EEG system, volunteers were asked to 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. The cue sequence for each trial was pseudo-random and different for each block. After each block, a pause was observed and subjects had to press the space bar to continue. The participants were presented with fifteen blocks of four trials for each of the four conditions (burst or msequence x 40% or 100%). The task was implemented in Python using the Psychopy toolbox. The four discs were all 150 pixels, without borders, and were presented on the following LCD monitor: Dell P2419HC, 1920 x 1080 pixels, 265 cd/m2, and 60 Hz refresh rate. After completing the experiment and removing the EEG equipment, the participants were asked to provide subjective ratings for the different stimuli conditions. These stimuli included burst c-VEP with 100% amplitude, burst c-VEP with 40% amplitude, m-sequences with 100% amplitude, and m-sequences with 40% amplitude. Each stimulus was presented three times in a pseudo-random order. Following the presentation of each stimulus, participants were presented with three 11-points scales and were asked to rate the visual comfort, visual tiredness, and intrusiveness using a mouse. In total, participants completed 12 ratings (3 repetitions x 4 types of stimuli) for each of the three scales.

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Citation & Impact

Stimulus Protocol
../_images/CastillosCVEP40.svg

2.2s task window per trial · 2-class c-vep paradigm · 1 runs/session across 1 sessions

HED Event Tags
HED tags2/2 events annotated

Source: MOABB BIDS HED annotation mapping.

Experimental-stimulus
2
Label
2
Sensory-event
2
Visual-presentation
2
0
Sensory-eventExperimental-stimulusVisual-presentationLabel
1
Sensory-eventExperimental-stimulusVisual-presentationLabel

HED tree view

Tree · 0
├─ Sensory-event
├─ Experimental-stimulus
├─ Visual-presentation
└─ Label
Tree · 1
├─ Sensory-event
├─ Experimental-stimulus
├─ Visual-presentation
└─ Label
Channel Summary
Total channels32
EEG32 (EEG)
Montagestandard_1020
Sampling500 Hz
ReferenceFCz
Filter{'line_noise_filter': 'IIR cut-band filter 49.9-50.1 Hz, order 16'}
Notch / line50 Hz

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

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

Dataset [1] from the study on burst-VEP [2].

Dataset description

Participants were comfortably seated and instructed to read and sign the informed consent. EEG data were recorded using a BrainProduct LiveAmp 32 active electrodes wet-EEG setup with a sample rate of 500 Hz to record the surface brain activity. The 32 electrodes were placed following the 10–20 international system on a BrainProduct Acticap. The ground electrode was placed at the FPz electrode location and all electrodes were referenced to the FCz electrode. The impedance of all electrodes was brought below 25kOhm prior to recording onset. Once equipped with the EEG system, volunteers were asked to 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. The cue sequence for each trial was pseudo-random and different for each block. After each block, a pause was observed and subjects had to press the space bar to continue. The participants were presented with fifteen blocks of four trials for each of the four conditions (burst or msequence x 40% or 100%). The task was implemented in Python using the Psychopy toolbox. The four discs were all 150 pixels, without borders, and were presented on the following LCD monitor: Dell P2419HC, 1920 x 1080 pixels, 265 cd/m2, and 60 Hz refresh rate. After completing the experiment and removing the EEG equipment, the participants were asked to provide subjective ratings for the different stimuli conditions. These stimuli included burst c-VEP with 100% amplitude, burst c-VEP with 40% amplitude, m-sequences with 100% amplitude, and m-sequences with 40% amplitude. Each stimulus was presented three times in a pseudo-random order. Following the presentation of each stimulus, participants were presented with three 11-points scales and were asked to rate the visual comfort, visual tiredness, and intrusiveness using a mouse. In total, participants completed 12 ratings (3 repetitions x 4 types of stimuli) for each of the three scales.

References

[1]

Kalou Cabrera Castillos. (2023). 4-class code-VEP EEG data [Data set]. Zenodo.(dataset). DOI: https://doi.org/10.5281/zenodo.8255618

[2]

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

from moabb.datasets import CastillosCVEP40
dataset = CastillosCVEP40()
data = dataset.get_data(subjects=[1])
print(data[1])

Dataset summary

#Subj

12

#Chan

32

#Trials / class

15/15/15/15

Trials length

2.2 s

Freq

500 Hz

#Sessions

1

#Trial classes

4

#Epochs classes

2

#Epochs / class

3525 NT / 3495 T

Codes

m-sequence

Presentation rate

60 Hz

Participants

  • Population: healthy

  • Age: 30.6 years

Equipment

  • Amplifier: BrainProducts LiveAmp 32

  • Electrodes: EEG

  • Montage: standard_1020

  • Reference: FCz

Preprocessing

  • Data state: raw

Data Access

Experimental Protocol

  • Paradigm: cvep

  • Task type: reactive BCI

  • Tasks: visual_attention

  • Feedback: none

  • Stimulus: visual flicker

Notes

Added in version 1.1.0.

__init__(window_size=0.25, subjects=None, sessions=None, **kwargs)[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, paradigm_type, path=None, force_update=False, update_path=None, verbose=None)[source]#

Return the data paths of a single subject.

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