Nonparametric Early Stopping Detection for c-VEP-based Brain-Computer Interfaces: A Pilot Study.

Brain-computer interface (BCI) systems based on code-modulated visual evoked potentials (c-VEP) stand out for achieving excellent command selection accuracies with very short calibration times. One of the natural steps to democratize their use in plug-and-play environments is to develop early stopping algorithms. These methods allow real-time detection of the minimum number of code repetitions needed to provide reliable selections. However, such techniques are scarce in the current state-of-the-art for c-VEP-based BCI systems based on the classical circular shifting paradigm. Here, a novel nonparametric early stopping method is proposed, which approximates the distribution of unattended commands to a normal distribution and issues a selection when the correlation of the command is considered an outlier. The proposal has been evaluated offline with 15 healthy users, achieving an average accuracy of 97.08% and a speed of 1.37 s/command. Likewise, the algorithm has also been evaluated with an additional user in an online way, as a proof of concept to validate its technical feasibility, achieving an average accuracy of 96.88% with a speed of 1.67 s/command. These results suggest that the real time application of the proposed algorithm is feasible, significantly reducing the required selection time without compromising accuracy.