: 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. 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. 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. 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.
Tresols et al. (Mon,) studied this question.