Psychophysics for the Detection of Temporal Fine Structure Performance in Normal Hearing and Cochlear Implant Users

A cochlear implant (CI) is a device that provides people with severe hearing
loss or deafness the ability to acquire functional hearing by electrically
stimulating the auditory nerve. CI users have a good understanding of speech in
quiet situations. One of the most pressing problems is the understanding of
speech in real life conditions, where there often is competing noise. Other
difficult conditions include pitch variation in tonal languages, emotion in
speech (prosody), sound localization and music perception (Dincer D'Alessandro
et al., 2018; Liu et al., 2017; Moore, 2008; Rubinstein, 2004).
Sound can basically be split into an envelope (variation of amplitude over
time) and into temporal fine structure (TFS, representing variation in
frequency and phase over time). CIs mainly encode the envelope and only a very
limited amount of TFS. The ability to detect temporally encoded frequency
information helps to detect the fundamental frequency, to perceive subtle pitch
variation and to perceive timbre differences in music. With the inclusion of
TFS, the sound quality of CIs would greatly improve. The TEMPORAL project is
targeting this very important problem by applying AI techniques to create or
modify speech encoding strategies to optimize listening in difficult
situations.

The aim of this current proposal is to compare test methods for determining TFS
performance, to establish detection thresholds for both CI users and normal
hearing subjects and to produce normative data for the computer model and
machine learning system. A wide range of psychophysical tests exists to
directly and indirectly test TFS performance. Methods to test TFS directly were
developed by Moore and colleagues, the TFS1 (Moore & Sek, 2009) and TFS-LF
(Hopkins & Moore, 2010). Other tests focus on areas of improvement such as
speech recognition in noise (Houben et al., 2014; Smits et al., 2013), pitch
perception (Snel-Bongers et al., 2011; Vaerenberg et al., 2011) and music
perception (Moon & Hong, 2014) and are indirect ways of detecting TFS. While
these psychophysical tests have been used separately, no direct comparison has
been made. Such a comparison provides insight in the relative performance of
several aspects of hearing. It will also show which test is optimal for
determining TFS performance. All subjects will undergo tests that are selected
for their capability to inform TFS performance and provide input for
computational models. In addition, tests are included that measure speech
performance. Selected tests are all acoustical, meaning they are played through
speakers or headphones. TFS tests compared with speech tests will provide
valuable information on which tests are suitable for this purpose. Results from
TFS tests provide baseline values for input in the computer models and further
research. To limit contamination of the data by cognitive factors and ease of
implementation into the machine learning system, we will focus on task-specific
(discrimination tasks or n-alternative forced choice) psychophysical
experiments.

This study is a prospective single-centred cross-sectional study. The total
duration of the study is estimated to be 2.5 years. CI users and normal-hearing
subjects are included as study group. Each subject will participate in a
maximum of 3 non-consecutive test days. Each test day consists of a test
session that lasts for approximately 3 hours, with breaks between each test and
additional breaks if needed. The setting is the LUMC ENT department where there
is long-standing experience with psychophysical testing.

This study is performed on CI users and normal hearing subjects. The study is
considered to involve negligible risk and minimal burden. Benefits to the CI
population at large may be a better and more efficient method of testing TFS
performance. Given the small risk and high yield of this research it is an
ethically justified study.