Noise-Driven Feature Enhancement of High-Frequency SSVEP Through Underdamped Second-Order Stochastic Resonance Energy...

Brain-computer interface (BCI) systems leveraging high-frequency steady-state visual evoked potentials (SSVEP) enhance user comfort, but their signals suffer from significant amplitude attenuation, which challenges precise detection. To address this, we proposed a novel weak-feature enhancement framework, FBUSSR-CCA, which integrates a monostable (single-well) underdamped second-order stochastic resonance (USSR) model into the filter-bank processing pipeline. This model strategically converts noise energy to nonlinearly amplify weak, high-frequency target components. Validation was performed on two public high-frequency SSVEP datasets (stimulus frequencies of 40-43Hz and 40-41.5Hz, respectively). The proposed FBUSSR-CCA was compared against six benchmark methods, including the best-performing FBCCA. Results indicated that the proposed framework attained superior performance across all three experimental conditions: the mean accuracy and ITR reached $83.61~\pm ~13.16$ % / $17.92~\pm ~7.81$ bits ${\mathrm{min}}^{-{1}}$ , $98.89~\pm ~2.17$ % / $28.77~\pm ~2.30$ bits ${\mathrm{min}}^{-{1}}$ , and $70.27~\pm ~19.02$ % / $11.88~\pm ~8.62$ bits ${\mathrm{min}}^{-{1}}$ , respectively. Further analysis confirmed the USSR module is a general-purpose tool (enhancing all six methods) and is parametrically robust to perturbations. These findings show that the monostable USSR framework is a reliable solution for enhancing high-frequency SSVEP, advancing the potential for more comfortable and efficient BCI systems.