Improve P300-speller performance by online tuning stimulus onset asynchrony (SOA)

Abstract

Abstract Objective. The P300-Speller is a classic brain–computer interface paradigm that has been subjected to numerous clinical trials. Some studies have reported that the performance of the P300-Speller is closely related to stimulus onset asynchrony (SOA), but very few studies have attempted to improve the performance of the P300-Speller by optimizing SOA. Approach. In this paper, we designed a P300-Speller system based on a variable SOA and dynamic stop strategy, which can automatically adjust SOA according to real-time operational performance. Main results. The online experiment results of 18 subjects showed that the event-related potential classifier and the dynamic stop algorithm established at 200 ms SOA can maintain the performance at a certain level among 50–300 ms SOA. The system can then reduce the SOA from an initial 200 ms to an average of about 98.5 ms while maintaining letter output accuracy. The average theoretical information transfer rate was significantly improved from 42.4 to 85 bit min−1 (the maximum was 232 bit min−1). Significance. These results demonstrate that the system established in this paper can automatically optimize the SOA settings, and this personalized SOA adjustment can effectively improve the performance of the P300-Speller.