Authors: Research Scholar Ms. Monali Khune, Dr. Amol Y. Deshmukh
Abstract: Background: Brain–computer interface (BCI) systems frequently utilize P300 and steady-state visual evoked potential (SSVEP) paradigms. Because neither approach yields universal success across users, recent research has explored hybrid BCI designs that merge multiple techniques to expand user compatibility. Although hybrid P300/SSVEP systems are a relatively recent innovation with limited performance optimization studies to date, they represent a promising avenue for improving system accessibility. New method: In this research, we contrast a conventional hybrid P300/SSVEP BCI framework with an innovative approach. Specifically, shape alterations are utilized instead of color modulations to evoke the P300 wave, aiming to minimize any adverse impact on SSVEP signal strength. Result: The new hybrid paradigm presented in this paper yields much better performance than the traditional hybrid paradigm. Comparison with existing method: The novel hybrid paradigm yields an SSVEP classification improvement of close to 20% over the standard approach. Furthermore, all tested paradigms—excluding the conventional hybrid model—achieve a perfect 100% accuracy rate in P300 classification. Conclusions: The innovative hybrid P300/SSVEP brain-computer interface paradigm replaces traditional color-shifting stimuli with shape-altering visual elements, matching the classification accuracy of conventional SSVEP and P300 setups. Furthermore, the author explored how presenting multiple visual stimuli at once triggers overlapping brain responses and evaluated the resulting interference on signal detection.
