Enhancing SSVEP-BCI performance through multi-stimulus discriminant fusion analysis.

Abstract

<i>Objective.</i>To enhance frequency recognition in steady-state visual evoked potential (SSVEP)-based brain-computer interfaces (BCIs), particularly under short data acquisition and complex environmental conditions.<i>Approach.</i>We propose multi-stimulus discriminant fusion analysis (MSDFA), a novel method that integrates multi-stimulus strategies with discriminant modeling. MSDFA was evaluated on two public datasets (Benchmark and BETA) and compared with conventional approaches including eCCA, eTRCA, and their variants.<i>Main results.</i>MSDFA consistently outperformed existing methods across different data lengths and training block quantities. It achieved maximum information transfer rates of 247.17 ± 10.15 bpm on the Benchmark dataset and 192.72 ± 9.44 bpm on the BETA dataset, demonstrating superior robustness and efficiency.<i>Significance.</i>By combining complementary algorithmic strengths, MSDFA improves adaptability to individual variability and complex environments, advancing the practical utility and reliability of SSVEP-BCI systems.