Abstract：The spatial modulation characteristics of visual evoked potentials (VEPs) provide an effective approach for designing user-friendly and practical brain-computer interface systems. However, the spatially modulated VEPs are usually weak and have low signal-to-noise ratios, making it crucial to study how to efficiently elicit and accurately identify weak spatially modulated VEPs. In this study, we employed visual stimuli with a radius smaller than 0.5° of visual angle and designed two mixed spatial modulation induction paradigms including the "transient stimuli serial induction paradigm" and the "steady-state and transient stimuli parallel induction paradigm". Twelve healthy subjects participated in the experiments, and the two paradigms were quantitatively compared by calculating the spatial modulated signal-to-noise ratio (sm-SNR) and offline classification accuracy. Results indicated that the average sm-SNR of the "Low-Frequency Transient" spatial modulation feature under the "transient stimuli serial induction paradigm" could reach 0.0148, significantly higher than that under the "steady-state and transient stimuli parallel induction paradigm" for the "low-frequency transient", "high-frequency steady-state", and their mixed spatial modulation features. The offline classification results were consistent with the feature analysis results, showing that the spatial modulation VEPs recognition accuracy was higher under the "transient stimuli serial induction paradigm", with an average classification accuracy of 85%. This study is expected to provide a reference for the design of high-performance visual brain-computer interfaces based on the spatial modulation characteristics of VEPs.
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