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

Objective.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.Approach.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.Main results.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.Significance.By combining complementary algorithmic strengths, MSDFA improves adaptability to individual variability and complex environments, advancing the practical utility and reliability of SSVEP-BCI systems.