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Updated: Jul 18, 2025

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Combining brain-computer interfaces and multiplayer video games: an application based on c-VEPs.

Selene Moreno-Calderón1, Víctor Martínez-Cagigal1,2, Eduardo Santamaría-Vázquez1,2

  • 1Biomedical Engineering Group (GIB), E.T.S Ingenieros de Telecomunicación, University of Valladolid, Valladolid, Spain.

Frontiers in Human Neuroscience
|August 21, 2023
PubMed
Summary
This summary is machine-generated.

This study developed a multiplayer video game using brain-computer interfaces (BCI) with code-modulated visual evoked potentials (c-VEPs) for enhanced accessibility. The c-VEP BCI system achieved high accuracy and user satisfaction in a competitive "Connect 4" game, demonstrating superior performance.

Keywords:
brain-computer interfacescode-modulated visual evoked potentialselectroencephalographymultiplayervideo games

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Area of Science:

  • Neuroscience and Human-Computer Interaction
  • Development of assistive technologies for individuals with motor disabilities

Background:

  • Video games are a significant entertainment medium but pose accessibility challenges for individuals with severe motor impairments.
  • Brain-computer interfaces (BCI) offer a potential solution by enabling control through brain signals.
  • Code-modulated visual evoked potentials (c-VEPs) represent an advanced BCI control signal method, yet their application in video games remains unexplored.

Purpose of the Study:

  • To design, develop, and evaluate a multiplayer "Connect 4" video game controlled by a c-VEP-based BCI.
  • To assess the feasibility and performance of c-VEP BCIs in a competitive gaming environment.
  • To explore the impact of competitiveness and motivation on BCI control.

Main Methods:

  • A real-time BCI system processed electroencephalograms (EEGs) sequentially for two users.
  • Game column selection utilized a c-VEP paradigm with pseudorandom binary code shifting.
  • Usability was evaluated through individual and competitive play sessions with 22 healthy users, measuring accuracy, speed, satisfaction, and workload.

Main Results:

  • The c-VEP BCI achieved an average accuracy of 93.74% ± 1.71% with a selection time of 5.25 seconds.
  • User questionnaires indicated minimal perceived workload and high satisfaction.
  • The application was described as intuitive, responsive, and smooth.

Conclusions:

  • The developed c-VEP-based multiplayer video game demonstrated effective performance and high user engagement.
  • The system supports motivation and minimal workload, outperforming other control signal versions of "Connect 4".
  • This work validates c-VEPs as a viable and effective control method for accessible, competitive BCI gaming.