moabb.datasets.CastillosBurstVEP100#

class moabb.datasets.CastillosBurstVEP100[source]#

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

Dataset summary

#Subj

#Chan

#Trials / class

Trials length

Sampling rate

#Sessions

#Trial classes

#Epochs classes

#Epochs / class

Codes

Presentation rate

12

32

15/15/15/15

2.2s

500Hz

1

4

2

5820 NT / 1200 T

Burst-CVEP

60Hz

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

Notes

New in version 1.1.0.