# Implementation of a passive code-VEP Brain Computer Interface with transparent flickers

Pietro Cimarosto, Kalou Cabrera Castillos, Sébastien Velut, Raphaëlle N Roy, Frédéric Dehais

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Pietro Cimarosto, Kalou Cabrera Castillos, Sébastien Velut, Raphaëlle N Roy, Frédéric Dehais. Implementation of a passive code-VEP Brain Computer Interface with transparent flickers. Cortico, Laurent Bougrain; Camille Jeunet; Valérie Marissens Cueva; Laëtitia Raison-Aubry; Sébastien Rimbert; Raphaëlle Roy, May 2024, Nancy, France. hal-05326338

HAL Id: hal-05326338

https://hal.science/hal-05326338v1

Submitted on 22 Oct 2025

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# Implementation of a passive code-VEP Brain Computer Interface with transparent flickers

Pietro Cimarosto, Kalou Cabrera Castillos, Sébastien Velut, Raphaëlle N. Roy & Frédéric Dehais

Fédération ENAC ISAE-SUPAERO ONERA, Université de Toulouse, France

Code Visually Evoked Potentials (c-VEP) have gained momentum in the Brain-Computer Interface (BCI) community. This innovative method leverages pseudo-random visual flickers, offering numerous benefits including reduced calibration times [1]. Traditionally, c-VEP has been predominantly applied to facilitate hands-free interactions within reactive BCI systems. However, we propose a novel application of this paradigm, utilizing c-VEP within passive BCI systems for the purpose of spatial attention tagging. To achieve this, our team has successfully developed transparent flickers to ensure that attention tagging can be conducted without causing distraction or eye strain for the user through overt and intense flashes [2]. This was accomplished by diminishing the contrast and intensity of the stimuli, specifically by reducing their amplitude modulation depth, thus presenting them at a periliminal threshold where they remain just below the level of conscious perception [3,4]. In the present study, our goal is to shift this paradigm to more ecological settings such as the The Multi Attribute Task Battery (MATB). The MATB interface comprises three sub-tasks: Monitoring, Tracking, Communications. Participants are tasked with completing these sub-tasks to achieve the highest possible performance by maintaining a cursor within a rectangular target for Tracking, correctly adjusting radio frequencies as per auditory instructions for Communications, and responding to visual alarms for Monitoring. These tasks are executed using a joystick and a computer keyboard. Additionally, participants engage in an auditory "oddball" task, pressing the joystick trigger upon hearing a rare sound (25% occurrence) amidst frequent sounds (75% occurrence). Three stimuli are presented imperceptibly over the sub-tasks of Tracking, Monitoring, and Communications (refer to figure 1). These stimuli flash aperiodically with unique codes (sequences of 1 - on, and 0 - off), with minimized correlation among them. The intensity of responses evoked by each transparent stimulus should not only identify which MATB field is being observed at any moment but also measure the depth of information processing through the synchronicity between the stimulation frequency of a MATB field and neural activity. In the present phase of our project, we have utilized the Timeflux [5] framework and developed our own open-source tools to achieve real-time estimation of attention during the MATB tasks. Our current system is capable of displaying three transparent flicker overlay using our "flicker on top" application [6] on the MATB interface and streaming essential information (such as the timing and code of the flickers, along with EEG data) to Timeflux. This setup enables Timeflux to predict the user's spatial attention effectively after a short 1min calibration phase. Currently, this framework is being tested with an 8-channel dry EEG system and is slated for future application with a more sophisticated 32-channel wet EEG system for data collection efforts related to the PROTEUS ANR project. These tests will be conducted in both laboratory environments and within the ISAE-SUPAERO flight simulator, promising a comprehensive evaluation of the framework's capabilities in realistic settings.

[1] Cabrera Castillos, K., Ladouce, S., Darmet, L., & Dehais, F. (2023). Burst c-VEP Based BCI: Optimizing stimulus design for enhanced classification with minimal calibration data and improved user experience. *NeuroImage*, *284*, 120446.

[2] Ladouce, S., Darmet, L., Torre Tresols, J. J., Velut, S., Ferraro, G., & Dehais, F. (2022). Improving user experience of SSVEP BCI through low amplitude depth and high frequency stimuli design. *Scientific Reports*, *12*(1), 8865.

[3] Ladouce, S., & Dehais, F. (2024). Frequency tagging of spatial attention using periliminal flickers. *Imaging neuroscience*, *2*, 1-17.
[4] Dehais, F., Castillos, K. C., Ladouce, S., & Clisson, P. (2024). Leveraging textured flickers: a leap toward practical, visually comfortable, and high-performance dry EEG code-VEP BCI. Journal of Neural Engineering, 21(6), 066023.

[5] Clisson, P., Bertrand-Lalo, R., Congedo, M., Victor-Thomas, G., & Chatel-Goldman, J. (2019, September). Timeflux: an open-source framework for the acquisition and near real-time processing of signal streams. In *BCI 2019-8th International Brain-Computer Interface Conference*.

[6] https://github.com/neuroergoISAE/FlickersOnTop

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