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An online user-centric brain–computer interface based on code-modulated visually evoked potentials and partially observable Markov decision process
Journal article   Open access   Peer reviewed

An online user-centric brain–computer interface based on code-modulated visually evoked potentials and partially observable Markov decision process

Juan Jesús Torre Tresols, Caroline Ponzoni Carvalho Chanel and Frédéric Dehais
Engineering applications of artificial intelligence, v 175, 114575
01 Jul 2026
DOI: https://doi.org/10.1016/j.engappai.2026.114575
Featured in Collection :   Drexel's Newest Publications
url
https://doi.org/10.1016/j.engappai.2026.114575View
Published, Version of Record (VoR) Open CC BY V4.0

Abstract

Brain–computer interface (BCI) Code-modulated visual evoked potential (c-VEP) Electroencephalography (EEG) Online BCI Partially observable Markov decision process (POMDP) User-centered design
Background and Objectives: Reactive brain–computer interfaces (rBCIs) can deliver fast and reliable performance, yet the decision step — determining when to act based on neural evidence — remains an often overlooked component. In prior work, we proposed a decision-making framework based on partially observable Markov decision processes (POMDP). The present study moves this approach from offline validation to a real-time setting, integrating it into a code-modulated visual evoked potential (c-VEP) rBCI. Methods: Twelve healthy participants performed a five-class c-VEP control task across two sessions, each comprising a cued and a self-paced (Pinpad) task with a semi-dry electroencephalography (EEG) system. One session used a conventional accumulation-based decision strategy; the other employed the POMDP-based approach. In the POMDP condition, calibration data were collected during an engaging cued task, allowing the policy to be computed without interrupting user interaction. Results: Across all tasks and conditions, participants achieved mean accuracies above 97%. In the self-paced task, the POMDP significantly reduced mean decoding time (1.55 s) compared to the accumulation baseline (1.97 s), while maintaining equivalent accuracy. Conclusion: This study provides the first online demonstration of a POMDP-based decision framework for rBCI, balancing speed and accuracy under the constrains of real-time operation. By removing the need for individual thresholds and integrating calibration into active use, the approach offers a flexible, user-centric pathway toward more user-centered and practical BCIs.

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