Objectives: Effort investment during listening varies as a function of task demand and motivation. Several studies have manipulated both these factors to elicit and measure changes in effort associated with listening. The cardiac pre-ejection period (PEP) is a relatively novel measure in the field of cognitive hearing science. This measure, which reflects sympathetic nervous system activity on the heart, has previously been implemented during a tone discrimination task but not during a speechin-noise task. Therefore, the primary goal of this study was to explore the influences of signal to noise ratio (SNR) and monetary reward level on PEP reactivity during a speech-in-noise task. Design: Thirty-two participants with normal hearing (mean age = 22.22 years, SD = 3.03) were recruited at VU University Medical Center. Participants completed a Dutch speech-in-noise test with a singleinterfering-talker masking noise. Six fixed SNRs, selected to span the entire psychometric performance curve, were presented in a block-wise fashion. Participants could earn a low (€0.20) or high (€5.00) reward by obtaining a score of ≥70% of words correct in each block. The authors analyzed PEP reactivity: the change in PEP measured during the task, relative to the baseline during rest. Two separate methods of PEP analysis were used, one including data from the whole task block and the other including data obtained during presentation of the target sentences only. After each block, participants rated their effort investment, performance, tendency to give up, and the perceived difficulty of the task. They also completed the need for recovery questionnaire and the reading span test, which are indices of additional factors (fatigue and working memory capacity, respectively) that are known to influence listening effort. Results: Average sentence perception scores ranged from 2.73 to 91.62%, revealing a significant effect of SNR. In addition, an improvement in performance was elicited by the high, compared to the low reward level. A linear relationship between SNR and PEP reactivity was demonstrated: at the lower SNRs PEP reactivity was the most negative, indicating greater effort investment compared to the higher SNRs. The target stimuli method of PEP analysis was more sensitive to this effect than the block-wise method. Contrary to expectations, no significant impact of reward on PEP reactivity was found in the present dataset. Also, there was no physiological evidence that participants were disengaged, even when performance was poor. A significant correlation between need for recovery scores and average PEP reactivity was demonstrated, indicating that a lower need for recovery was associated with less effort investment. Conclusions: This study successfully implemented the measurement of PEP during a standard speech-in-noise test and included two distinct methods of PEP analysis. The results revealed for the first time that PEP reactivity varies linearly with task demand during a speech-in-noise task, although the effect size was small. No e...

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People with hearing impairment typically have difficulties following conversations in multi-talker situations. Previous studies have shown that utilizing eye gaze to steer audio through beamformers could be a solution for those situations. Recent studies have shown that in-ear electrodes that capture electrooculography in the ear (EarEOG) can estimate the eye-gaze relative to the head, when the head was fixed. The head movement can be estimated using motion sensors around the ear to create an estimate of the absolute eye-gaze in the room. In this study, an experiment was designed to mimic a multi-talker situation in order to study and model the EarEOG signal when participants attempted to follow a conversation. Eleven hearing impaired participants were presented speech from the DAT speech corpus (Bo Nielsen et al., 2014), with three targets positioned at −30 • , 0 • and +30 • azimuth. The experiment was run in two setups: one where the participants had their head fixed in a chinrest, and the other where they were free to move their head. The participants' task was to focus their visual attention on an LED-indicated target that changed regularly. A model was developed for the relative eye-gaze estimation, taking saccades, fixations, head movement and drift from the electrode-skin half-cell into account. This model explained 90.5% of the variance of the EarEOG when the head was fixed, and 82.6% when the head was free. The absolute eye-gaze was also estimated utilizing that model. When the head was fixed, the estimation of the absolute eye-gaze was reliable. However, due to hardware issues, the estimation of the absolute eye-gaze when the head was free had a variance that was too large to reliably estimate the attended target. Overall, this study demonstrated the potential of estimating absolute eye-gaze using EarEOG and motion sensors around the ear.

The duration of the QT interval and TpTe are closely related. Drugs appear to prolong the TpTe interval as a predictable fraction of the total QT prolongation.

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