Characterization and Classification of the Frequency Following Response to Vowels at Different Sound Levels in Normal Hearing Adults

• Main Author: Heffernan, Brian
• Other Authors: Giguère, Christian
• Format: Others
• Language: en
• Published: Université d'Ottawa / University of Ottawa 2019
• Subjects: Frequency following response; Speech; Brain-computer interfacing; Brainstem; Sound level; Machine learning
• Online Access: http://hdl.handle.net/10393/38816; http://dx.doi.org/10.20381/ruor-23068

This work seeks to more fully characterize how the representation of English vowels changes with increasing sound level in the frequency following response (FFR) of normal-hearing adult subjects. It further seeks to help inform the design of brain-computer interfaces (BCI) that exploit the FFR for hearing aid (HA) applications. The results of three studies are presented, followed by a theoretical examination of the potential BCI space as it relates to HA design. The first study examines how the representation of a long vowel changes with level in normal hearing subjects. The second study examines how the representation of four short vowels changes with level in normal hearing subjects. The third study utilizes machine learning techniques to automatically classify the FFRs captured in the second study. Based in-part on the findings from these three studies, potential avenues to pursue with respect to the utilization of the FFR in the automated fitting of HAs are proposed. The results of the first two studies suggest that the FFR to vowel stimuli presented at levels in the typical speech range provide robust and differentiable representations of both envelope and temporal fine structure cues present in the stimuli in both the time and frequency domains. The envelope FFR at the fundamental frequency (F0) was generally not monotonic-increasing with level increases. The growth of the harmonics of F0 in the envelope FFR were consistent indicators of level-related effects, and the harmonics related to the first and second formants were also consistent indicators of level effects. The third study indicates that common machine-learning classification algorithms are able to exploit features extracted from the FFR, both in the time and frequency domains, in order to accurately predict both vowel and level classes among responses. This has positive implications for future work regarding BCI-based approaches to HA fitting, where controlling for clarity and loudness are important considerations.