Abstract:
To objectively evaluate weak neural responses such as auditory steady-state responses (ASSRs) often involves repetitive measurements, after which the accumulated trials are averaged or analyzed by statistical methods to detect a significant response. Such detection methods are often performed off-line on all measured trials. However, the number of required trials, and therefore the measurement time, is often heuristically chosen, and might be suboptimal. In this work, we compared three real-time signal-detection algorithms that could yield improved detection performance, at reduced measurement time. The classical Neyman-Pearson (NP) detector was evaluated by quantifying the signal-to-noise ratio (SNR), and detection probability of ASSRs, as a function of accumulated trial number. We also analyzed the performance of the Bayes factor (BF) to detect ASSRs at different thresholds. Finally, we modified the sequential probability ratio test (SPRT), with a cropped maximum likelihood (ML) estimator, such that it can detect ASSRs (with unknown SNRs) sequentially. We compared the three real-time detectors by using Monte Carlo simulation, and evaluated their performance on detecting ASSRs, generated from the superposition of a pair of amplitude-modulated (AM) tones near 40 Hz in the EEG of nine subjects with normal hearing. The low-order ASSRs (i.e., envelope frequency-following responses) have been sufficiently evaluated in the literature. However, the higher-order ASSRs might reflect further nonlinear mechanisms in the upper ascending auditory pathway. Results show that the real-time detectors can detect not only all low-order ASSRs but also higher-order ASSRs at frequencies with lower SNR. The NP detector yielded the best simulation and actual detection performance. For all subjects, the second-order ASSRs could already be detected with the NP and BF detectors within five trials, but more trials were needed for the modified SPRT detector. In general, higher-order ASSRs requ