Improved Detection of Vowel Envelope Frequency Following Responses Using Hotelling’s T2 Analysis

Objectives: Objective detection of brainstem responses to natural speech stimuli is an important tool for the evaluation of hearing aid fitting, especially in people who may not be able to respond reliably in behavioral tests. Of particular interest is the envelope frequency following response (eFFR), which refers to the EEG response at the stimulus’ fundamental frequency (and its harmonics), and here in particular to the response to natural spoken vowel sounds. This article introduces the frequency-domain Hotelling’s T2 (HT2) method for eFFR detection. This method was compared, in terms of sensitivity in detecting eFFRs at the fundamental frequency (HT2_F0), to two different single-channel frequency domain methods (F test on Fourier analyzer (FA) amplitude spectra [FA-F-Test] and magnitude-squared coherence [MSC]) in detecting envelope following responses to natural vowel stimuli in simulated data and EEG data from normal-hearing subjects. Sensitivity was assessed based on the number of detections and the time needed to detect a response for a false-positive rate of 5%. The study also explored whether a single-channel, multifrequency HT2 (HT2_3F) and a multichannel, multifrequency HT2 (HT2_MC) could further improve response detection. Design: Four repeated words were presented sequentially at 70 dB SPL LAeq through ER-2 insert earphones. The stimuli consisted of a prolonged vowel in a /hVd/ structure (where V represents different vowel sounds). Each stimulus was presented over 440 sweeps (220 condensation and 220 rarefaction). EEG data were collected from 12 normal-hearing adult participants. After preprocessing and artifact removal, eFFR detection was compared between the algorithms. For the simulation study, simulated EEG signals were generated by adding random noise at multiple signal to noise ratios (SNRs; 0 to −60dB) to the auditory stimuli as well as to a single sinusoid at the fluctuating and flattened fundamental frequency (f0). For each SNR, 1000 sets of 440 simulated epochs were generated. Performance of the algorithms was assessed based on the number of sets for which a response could be detected at each SNR. Results: In simulation studies, HT2_3F significantly outperformed the other algorithms when detecting a vowel stimulus in noise. For simulations containing responses only at a single frequency, HT2_3F performs worse compared with other approaches applied in this study as the additional frequencies included do not contain additional information. For recorded EEG data, HT2_MC showed a significantly higher response detection rate compared with MSC and FA-F-Test. Both HT2_MC and HT2_F0 also showed a significant reduction in detection time compared with the FA-F-Test algorithm. Comparisons between different electrode locations confirmed a higher number of detections for electrodes close to Cz compared to more peripheral locations. Conclusion: The HT2 method is more sensitive than FA-F-Test and MSC in detecting responses to complex stimuli because it allows detection of multiple frequencies (HT2_F3) and multiple EEG channels (HT2_MC) simultaneously. This effect was shown in simulation studies for HT2_3F and in EEG data for the HT2_MC algorithm. The spread in detection time across subjects is also lower for the HT2 algorithm, with decision on the presence of an eFFR possible within 5 min.