Optimization of sequential test strategies for auditory Steady-State responses using Monte Carlo simulations for efficient tonal audiometry

This study investigates the detection of auditory steady-state responses for automatic tonal audiometry using sequential test strategies. Monte Carlo simulations are employed to optimize strategy parameters, aiming to minimize mean examination time while maintaining detection power. Optimization criteria are based on the areas under probability of detection and examination time versus signal-to-noise ratio curves. The optimal parameters obtained from simulations are compared with results from real EEG data. The simulated optimal settings closely matched those derived from real data, except for small epoch sizes, demonstrating the effectiveness of the proposed simulation-based optimization approach.