Impacts of Hearing Aid Use on Listening Effort and Motivation Using fNIRS
Phase: NA
Conditions: Hearing Loss
Sponsor: Sonova AG

Title: fNIRS, Listening Effort, and Motivation 
NCT number: NCT05893992 
Document date: May 5th 2025 
 

Design 
 
The experiment was a one-factor (two-level) repeated measures within-subjects design. 
The independent variable was hearing aid program, comprising (1) standard and (2) DNN-based 
programs as factor levels. The dependent variables, like our previous design 17, were listening 
accuracy, subjective listening effort, and HbDiff.  Listening accuracy was measured as percent of 
correctly repeated words. Listening effort was measured on a 7-point Likert scale 27 (1-no effort, 
2-very little effort, 3-little effort, 4-moderate effort, 5-considerable effort, 6-much effort, 7-extreme 
effort). HbDiff was measured using a prefrontal fNIRS system consisting of 6 active- and 2 short-
channel optodes positioned across the prefrontal cortex.  
Test materials 
 
Stimuli consisted of sequences of up to five R-SPIN sentences presented in noise. 
Sequences, rather than individual sentences, were used to leverage the superpositioning of the 
response functions for each sentence in the sequence to enhance the overall temporal summation, 
thus increasing the SNR of the overall hemodynamic response 28–31.  Sequences varied in number 
of sentences so that participants would be unable to anticipate which word to repeat, forcing them 
to attend to the entire sequence. Sequences of three or more sentences were comprised of low-
context sentences and were 18 or more seconds in duration. The shorter “decoy” sequences were 
comprised of one or two high-context sentences and were only included to create the impression 
that some sequences would contain only that number of sentences. The shorter high-context 
sequences’ data were not used for analysis. Noise consisted of multitalker babble and was gated 
on and off with each sentence. Speech was presented at individualized SNRs relative to the noise 
level, which was maintained at 70 dBA.     

Equipment 
Stimulus playback 
The experiment was conducted in a double-walled, sound-attenuated booth. The participant 
was seated in the center of the booth. The listening paradigm was administered using a custom 
MATLAB script (version R2021b) running on a Windows computer located outside the booth, 
which controlled stimulus playback, recorded participant responses, and transmitted stimulus onset 
and offset markers to the fNIRS recording software. The Windows computer was connected to an 
RME Fireface UCX sound card, which routed the stimuli to two self-powered Genelec 8030C 
speakers positioned at 0 and 180 degrees relative to the participant’s chair, each placed 1.3 meters 
away from the participant’s head. Speech was presented from the front speaker while noise was 
presented from the rear speaker.  
fNIRS device 
Cerebral oxygenation was measured using an Octamon continuous-wave fNIRS device 
(Artinis Medical Systems, Netherlands) with six active optodes and two-short channel optodes, 
consisting of eight LED diode emitters (wavelengths: 760 and 850 nm) and two photodiode 
detectors, sampled at 10 Hz. The light emitters and detectors were spaced approximately 3.5 cm 
apart, allowing penetration of about 1.75 cm into the cortex. The device was positioned over the 
participant’s prefrontal cortex. The base of the fNIRS headband was aligned just above the nasion, 
ensuring the lower medial light emitters were symmetrically positioned around the FpZ coordinate 
32. Recordings were transmitted via Bluetooth to the OxySoft software (version 3.2.70) on a 
Windows computer. The data were initially saved in .oxy4 format and later converted to .snirf 
format for analysis using MATLAB-integrated Homer3 scripts. 

Hearing aids 
The hearing aids used were Phonak Audéo I90-Sphere receiver-in-the-canal devices and 
coupled to participants’ ears using occluding silicone power domes. The hearing aids were 
programmed using Phonak’s proprietary gain formula and fine-tuned individually to NAL-NL2 
targets by a licensed audiologist. Phonak’s “Calm situation” and “Spheric speech in loud noise” 
programs, with all program features at default values, were used for the standard- and DNN-
listening conditions, respectively. The standard-listening program is the hearing aid’s default start-
up program and implements omnidirectional microphones, speech enhancement and fast-acting 
compression; features common across manufacturers’ standard-listening programs. The DNN-
listening condition implements a combination of directional microphones DNN-based noise 
management, and slow-acting compression. Hearing aids were worn for the full duration of testing.    
Procedure 
SNR-50 search 
Participants first determined the SNR at which they could correctly repeat 50% of the 
presented words (SNR-50). Individual SNR-50 measurements calibrated the “difficulty” of the 
task such that participants would perform equally despite differences in thresholds or other 
characteristics.  Participants wore the hearing aids in the standard-listening program during the 
SNR-50 search procedure. This procedure followed modified hearing-in-noise-test (HINT) SNR 
adaptation rules and the manual adaptive level controls from the Oldenburg Matrix Test. In each 
SNR-50 run, participants listened to 20 low-context R-SPIN sentences and repeated the final word 
of each. The SNR was adjusted dynamically - decreasing following a correct response and 
increasing after an incorrect one. The noise level was maintained at 70 dBA and the speech level 

varied systematically. The starting SNR was 8 dB. For the first five sentences, the SNR was 
adjusted in ±4 dB steps, while the remaining sentences were adjusted in ±2 dB increments. The 
SNR-50 was calculated as the mean SNR from sentences 12 to 20, plus the estimated SNR after 
the final trial. Each run consisted of 20 sentences randomly selected from the 25 available per R-
SPIN list, with sentences from only one list used per run. Participants completed a total of three 
runs, with the average SNR-50+2 dB of the last two runs used for testing. The additional 2 dB 
were added to increase performance above chance so that participants would answer correctly 
more frequently and feel more motivated during testing. Throughout the task, the experimenter 
recorded participants' responses in real time to assess accuracy and trigger the next sentence. 
Across participants, the average SNR-50 was -0.6 dB (SD = 3.9). 
fNIRS recording 
The experimenter then placed the fNIRS device on the participant and paired it with the 
Oxysoft recording software. The signal quality was verified using the software’s signal quality 
index algorithm, ensuring that the fNIRS recording SNR was above the noise floor and not 
saturated by ambient lighting. If the signal quality was insufficient, the experimenter adjusted the 
device fit.  
Next, the experimenter provided the instructions for the main study task to the participants. 
The experimenter advised participants that they would hear sequences of sentences. Participants 
were instructed to repeat the last word of the last sentence in the sequence and then provide the 
number corresponding to their average listening effort rating for all the sentences in the sequence. 
Participants were then told that there would be a 30-second rest period between sequences and 
were asked to minimize movement and mind-wandering during baseline periods and testing. The 

experimenter was able to postpone the start of the following sequence (therefore elongating the 
baseline recording) in case the participant moved during the rest period.   
The study task consisted of testing blocks, with each block containing a total of eight events 
or sentence sequences: six sequences of three or more low-context sentences and two “decoy” 
sequences of one or two high-context sentences. Each sequence was separated by a 30-second 
silent baseline recording period. Each block comprised sentences taken from a single R-SPIN list 
not used in the SNR-50 task.  Altogether, participants completed five blocks: a practice block and 
four test blocks. The practice block was completed using the standard-listening program and two 
of the remaining four test blocks were assigned to either the standard- or DNN-listening conditions, 
for a total 16 sequences per condition. Test block order and R-SPIN list/block assignment was 
randomized between participants.  
Analysis 
fNIRS data processing 
 
The fNIRS processing pipeline resembled our previous study’s pipeline, which was 
adapted from Zhou et al., and was entirely implemented using MATLAB. The exception between 
our studies was that the current design incorporated short-channel subtraction. The fNIRS 
processing occurred as follows:  
1. Removal of step-like noise. Brief losses of contact between the fNIRS headband and skin 
might produce abrupt steps in the fNIRS morphology and can negatively impact signal 
quality. These steps were removed by calculating the derivative of each channel’s intensity 
time series, and absolute values greater than two SDs over the derivative’s mean were 
zeroed. Channels were recalculated using the cumulative sum of the updated derivatives. 

2. Exclude channels of poor quality. Signal quality was assessed using the scalp coupling 
index (SCI). The SCI extracts the heartbeat from the two fNIRS wavelengths by bandpass 
filtering between 0.5 and 1.5 Hz and then correlating the two filtered wavelengths. A poor 
correlation suggests that the fNIRS measurements did not accurately capture the heartbeat 
and would therefore not contain other meaningful physiological responses. Channels with 
an SCI < 0.75 were rejected, resulting in 11.5% of channels being rejected across 
participants.   
3. Conversion of light intensity to optical density. Refer to Hupper et al. 
4. Correction of motion artifacts. Movements during testing risk physical displacement of 
the optodes from the participant’s head, risking noise in the data. The wavelet 
decomposition method was used to correct motion artifacts for any wavelet coefficients 
outside an interquartile range of 0.1.  
5. Conversion of optical density to hemoglobin concentration using the Modified Beer—
Lambert law. Refer to Delpy et al. 
6. Band-pass filter (0.01 – 1.5 Hz). Low-frequency noise (such as drift) and high-frequency 
noise (such as breathing or heart rate) was band-pass filtered from the fNIRS signal. 
7. Short-channel correction:  Each channel underwent short-channel correction such that a 
general linear model was fit to the channel’s fNIRS signal with the timeseries of the nearest 
short-channel (based on Euclidean distance) as the regressor. The physiological noise 
measured by the short-channel  was regressed out of each channel’s output.  
8. Band-pass filter (0.01 – 0.09 Hz). An additional band-pass filter was applied to the data 
to remove any physiological noise missed prior to short-channel subtraction.   

9. Averaging. HbO and HbR were calculated for each sequence of sentences. The 
hemodynamic response was measured as the average of 10 seconds following each 
sequence onset to the end of each sequence minus a 5-second baseline average prior to the 
start of the sequence. Ten seconds was chosen between sequence onset and measurement 
onset to allow for the hemodynamic response to reach its peak. Hemodynamic responses 
were then averaged across all low-context sequences for a given condition.  
10. Data for analysis. The fNIRS response used for statistical analysis was calculated as the 
cerebral oxygen exchange (HbDiff = HbO - HbR). Three subregions, each containing 
averages across two optodes, were used for analysis. Lateral regions contained two 
peripheral optodes for each respective side and the lower medial region contained the two 
medial optodes.    
Statistical modelling  
Aligned with previous literature, subjective listening effort ratings and listening accuracy 
scores were averaged across trials resulting in a mean listening effort value and a mean accuracy 
score per participant. Afterwards, mean listening accuracy scores were transformed to rationalized 
arcsin units to reduce clustering of scores near ceiling performance. To assess the effect of the 
hearing aid program on subjective listening effort and on listening accuracy, two paired samples t-
tests were conducted. A standardized effect size was calculated for each test using Cohen’s d. 
Blood oxygenation data (HbDiff) were modelled using multi-level modeling (MLM) 
because of the clustered data structure (i.e., high levels of covariance) due to the repeated 
measurement design. MLM estimates random effects for each cluster to provide unbiased model 
estimates (standard errors, regression coefficients, p-values). Furthermore, MLM is robust against 
cases of missing data, such as rejected channels falling below the SCI threshold46. In contrast, an 

Analysis of Variance (ANOVA) is a complete-case analysis where an entire participant’s data 
would be removed if the participant had any missing data from rejected channels. 
We conducted three MLMs with HbDiff as the outcome variable: an unconditional model 
and two a priori theory-driven models to assess the effect of the hearing aid program and whether 
the effect of the hearing aid program is moderated by subregions of the prefrontal cortex. The main 
predictor variable was the hearing aid program (standard vs. DNN) and an interaction term 
between the hearing aid program and prefrontal subregion (left lateral vs. lower medial vs. right 
lateral). Categorical variables (hearing aid program and prefrontal subregion) were dummy coded. 
Standard listening and the left lateral subregion were used as the reference group for the hearing 
aid program and prefrontal subregion, respectively. Simple slopes analysis was used to probe the 
interaction.  
To assess which model best fit the data, a nested modeling approach was used. This 
approach adds predictor variables to the previous model and assesses if the additional predictors 
significantly improve the amount of variance explained by the model using a likelihood ratio test 
(LRT). The Akaike information criterion (AIC) was also used to assess model fit with lower AIC 
values indicating a better fit to the data.  
All analyses were conducted in R version 4.4.2. MLMs were estimated using full 
information maximum likelihood (FIML) and conducted with the following packages: lme4, 
lmerTest4, performance and interactions. Paired samples t-tests were conducted using stats and 
Cohen’s d was calculated using lsr. All statistical analyses were two-tailed tests with alpha of .05.