Operate P300 speller when performing other task

Abstract

Objective. The P300 speller is a classic brain–computer interface (BCI) paradigm that has the potential to restore impaired motor control function. However, previous studies have confirmed that the letter recognition accuracy (LRA) of the P300 speller is a challenge when performing other tasks. Approach. To address this, we implemented a dynamic stopping strategy (DSS) to maintain the P300 speller LRA when performing multiple tasks simultaneously. Multiple tasks with dynamic workload levels were adopted to simulate the brain’s other thinking activities while operating P300 speller. A Bayes-based DSS offline model was built in single-task (only P300 speller task) and an online P300 speller system was established to test the DSS algorithm feasibility in dual-task. Main results. Online experimental results showed that the P300 speller with DSS could achieve a high LRA (96.9%) under dual-task, which was similar to single-task (98.7%, p = 0.126). Under dual-task, DSS dynamically adjusted the discriminant confidence according to the workload levels of the distraction tasks (correlation coefficient r = −0.68). Therefore, DSS can increase the repeated sequences to compensate for the reduction of P300 speller signal-to-noise ratio caused by parallel thinking activities. The average of repeated sequences increased significantly from 4.98 times under single-task to 6.22 times under dual-task (p < 0.005). These results indicated that the P300 speller feature is robust and the DSS model built in single-task maintained the applicability in various dual-tasks. Significance. Overall, this study provides a basis for the implementation of laboratory-developed BCI in real-world environments.