Improve P300 Speller Performance by Changing Stimulus Onset Asynchrony (SOA) without Retraining the Subject-Independent Model

Abstract

P300 speller is a famous brain-computer interface (BCI) method, which translates mental attention by identifying the event-related potentials evoked by target stimulus. To improve its efficiency, subject-independent classification models and dynamical stopping strategies have been introduced into P300 speller. However, it has still not been determined whether these methods remain effective when the configurations of visual stimuli are changed. This study investigates whether subject-independent dynamical stopping model (SIDSM) can maintain high efficiency in the case of stimulus onset asynchrony (SOA) change. The SIDSM was built on a 55-subject database, and the classification efficiency was tested online with 14 new subjects. During the online experiment, four SOA conditions were tested, one of which had the same SOA as the modeling data, while the other three had different SOA settings. The SIDSM obtained comparable classification accuracy under different SOA settings. Thus, the efficiency of information transmission can be significantly improved by changing SOA only, without retraining the model. These results suggest that SIDSM has good robustness to changes in stimulus settings and can provide P300 speller with good flexibility for individual optimization.