Oticon Medical (Denmark)

Elective removal of bone-anchored hearing implants and epistheses is uncommon, limiting the understanding of the biological factors contributing to adverse reactions, implant success or failure. Therefore, the tissue-level response at the implant interface remains poorly understood. This study examined 21 explanted bone-anchored implants and interfacial tissues from 19 patients using a controlled retrieval protocol. The clinical data were correlated with histological, immunohistochemical, microbiological, and structural analyses. Pain (12/19) and infection (7/19) were the most common reasons for elective implant retrieval and in 5 cases occurred together. All explants, apart from one, showed significant osseointegration and maintained stability in situ, independent of their design characteristics. Staphylococcus aureus was isolated in five patients—four of whom reported pain even without overt infection. Staphylococcus epidermidis was detected in two patients: one with pain and infection and the other asymptomatic with a resolved infection. S. aureus colonization was associated with intense inflammation, marked by the presence of neutrophils, iNOS+ macrophages, CD3 + T-cells, and CD20 + B-cells. Pain and infection—often involving Staphylococcus species—are key drivers of implant retrieval. These findings emphasize the complex immune response at the implant site and the diagnostic value of analysing electively retrieved implants for improving patient outcomes.

Previous studies have demonstrated the feasibility of estimating the speech reception threshold (SRT) based on electroencephalography (EEG), termed SRTneuro, in younger normal-hearing (YNH) participants. This method may support speech perception in hearing-aid users through continuous adaptation of noise-reduction algorithms. The prevalence of hearing impairment and thereby hearing-aid use increases with age. The SRTneuro estimation is based on envelope reconstruction accuracy, which has also been shown to increase with age, possibly due to excitatory/inhibitory imbalance or recruitment of additional cortical regions. This could affect the estimated SRTneuro. This study investigated the age-related changes in the temporal response function (TRF) and the feasibility of SRTneuro estimation across age. Twenty YNH and 22 older normal-hearing (ONH) participants listened to audiobook excerpts at various signal-to-noise ratios (SNRs) while EEG was recorded using 66 scalp electrodes and 12 in-ear-EEG electrodes. A linear decoder reconstructed the speech envelope, and the Pearson's correlation was calculated between the reconstructed and speech-stimulus envelopes. A sigmoid function was fitted to the reconstruction-accuracy-versus-SNR data points, and the midpoint was used as the estimated SRTneuro. The results show that the SRTneuro can be estimated with similar precision in both age groups, whether using all scalp electrodes or only those in and around the ear. This consistency across age groups was observed despite physiological differences, with the ONH participants showing higher reconstruction accuracies and greater TRF amplitudes. Overall, these findings demonstrate the robustness of the SRTneuro method in older individuals and highlight its potential for applications in age-related hearing loss and hearing-aid technology.

Following a conversation involves numerous cognitive processes. Speech needs to be processed, retained, encoded and an answer needs to be prepared. Hearing impairment leads to degraded speech signal, which results in the need for additional cognitive resources to processing and encoding. This is particularly true for cochlear implant (CI) users, where the auditory nerve is stimulated electrically. The present study examined the cognitive effort required for listening (listening effort) and retaining sentences in memory (memory effort) among CI users by means of pupil dilation. The aim of the present study was to evaluate how CI users allocate their cognitive resources in an auditory dual task and whether changes in pupil size can be indicative of this allocation. Understanding how cognitive effort is allocated during speech comprehension might be helpful in rehabilitation following a CI implantation. Nineteen German-speaking CI users performed a sentence final word identification and recall test while their pupil dilation was recorded. Participants listened to a sentence in four-talker babble background noise and repeated the final word of each sentence. Sentences were presented in lists of either three or six sentences, and at the end of each list, participants were asked to repeat back as many final words as possible and were evaluated in four conditions with varying list length (three versus six sentences) and the necessity of a recall (recall versus no recall). The pupil size was analyzed before each sentence (sentence baseline) and in terms of the mean pupil dilation relative to the sentence baseline (sentence dilation). The conditions were statistically compared by fitting a line to the pupil measures across groups of three sentences and contrasting the intercepts and slopes, respectively: First, the three sentence conditions were compared with the first three sentences (#1 to 3) of the six-sentence conditions with the factors recall and list length. Second, within the six-sentence conditions, the first half (sentences #1 to 3) was compared with the second half (sentences #4 to 6) with the factors recall and halves. Over the course of the lists, sentence baseline diverged between three and six sentences, which manifested in the slope of sentence baseline being significantly higher in three than six sentences. The slope of sentence baseline also differed significantly between the first compared with second halve. The slope within the second halve is more positive compared with the first halve. The recall-related divergence was most prominent in the second half of the six-sentence lists, which manifested in a significant interaction between recall and halves in the intercept of the sentence baseline. Sentence dilation did not significantly change across the list presentation. CI users appeared to allocate their memory effort in accordance with task demands, while their listening efforts remained unchanged. Our results indicated that effort allocation for listening and memory processing can be assessed in CI users using pupil dilation during the sentence final word identification and recall test.

Objective. This study aimed to investigate the potential of contrastive learning to improve auditory attention decoding (AAD) using electroencephalography (EEG) data in challenging cocktail-party scenarios with competing speech and background noise.Approach. Three different models were implemented for comparison: a baseline linear model (LM), a non-LM without contrastive learning (NLM), and a non-LM with contrastive learning (NLMwCL). The EEG data and speech envelopes were used to train these models. The NLMwCL model used SigLIP, a variant of CLIP loss, to embed the data. The speech envelopes were reconstructed from the models and compared with the attended and ignored speech envelopes to assess reconstruction accuracy, measured as the correlation between the reconstructed and actual speech envelopes. These reconstruction accuracies were then compared to classify attention. All models were evaluated in 34 listeners with hearing impairment.Results. The reconstruction accuracy for attended and ignored speech, along with attention classification accuracy, was calculated for each model across various time windows. The NLMwCL consistently outperformed the other models in both speech reconstruction and attention classification. For a 3-second time window, the NLMwCL model achieved a mean attended speech reconstruction accuracy of 0.105 and a mean attention classification accuracy of 68.0%, while the NLM model scored 0.096 and 64.4%, and the LM achieved 0.084 and 62.6%, respectively.Significance. These findings demonstrate the promise of contrastive learning in improving AAD and highlight the potential of EEG-based tools for clinical applications, and progress in hearing technology, particularly in the design of new neuro-steered signal processing algorithms.

The combination of directional microphones and noise reduction (DIR + NR) in hearing aids offers substantial improvement in speech intelligibility and reduction in listening effort in spatial acoustic scenarios. Pupil dilation can be used to infer ocular markers of listening effort. However, pupillometry is also known to crucially depend on luminance. The present study investigates the effects of a state-of-the-art DIR + NR algorithm (implemented in commercial hearing aids) on pupil dilation of hearing aid users both in darkness and ambient light conditions. Speech intelligibility and peak pupil dilations (PPDs) of 29 experienced hearing aid users were measured during a spatial speech-in-noise-task at a signal-to-noise ratio (SNR) matching the individual's speech reception threshold. While speech intelligibility improvements due to DIR + NR were substantial (about 35 percentage points) and independent of luminance, PPDs were only significantly reduced due to DIR + NR in ambient light, but not in darkness. This finding suggests that the reduction in PPD due to DIR + NR (most likely through improvement in SNR) is dependent on luminance and should be interpreted with caution as a marker for listening effort. Relations of reduction in PPD due to DIR + NR in ambient light to subjectively reported long-term fatigue, age, and pure-tone average were not statistically significant, which indicates that all patients benefitted similarly in listening effort from DIR + NR, irrespective of these patient-specific factors. In conclusion, careful control of luminance needs to be taken in hearing aid studies inferring listening effort from pupillometry data.

This study investigates the potential of speech reception threshold (SRT) estimation through electroencephalography (EEG) based envelope reconstruction techniques with continuous speech. Additionally, we investigate the influence of the stimuli's signal-to-noise ratio (SNR) on the temporal response function (TRF). Twenty young normal-hearing participants listened to audiobook excerpts with varying background noise levels while EEG was recorded. A linear decoder was trained to reconstruct the speech envelope from the EEG data. The reconstruction accuracy was calculated as the Pearson's correlation between the reconstructed and actual speech envelopes. An EEG SRT estimate (SRTneuro) was obtained as the midpoint of a sigmoid function fitted to the reconstruction accuracy versus SNR data points. Additionally, the TRF was estimated at each SNR level, followed by a statistical analysis to reveal significant effects of SNR levels on the latencies and amplitudes of the most prominent components. The SRTneuro was within 3 dB of the behavioral SRT for all participants. The TRF analysis showed a significant latency decrease for N1 and P2 and a significant amplitude magnitude increase for N1 and P2 with increasing SNR. The results suggest that both envelope reconstruction accuracy and the TRF components are influenced by changes in SNR, indicating they may be linked to the same underlying neural process.

Functional near-infrared spectroscopy (fNIRS) is an increasingly popular neuroimaging technique that measures cortical hemodynamic activity in a non-invasive and portable fashion. Although the fNIRS community has been successful in disseminating open-source processing tools and a standard file format (SNIRF), reproducible research and sharing of fNIRS data amongst researchers has been hindered by a lack of standards and clarity over how study data should be organized and stored. This problem is not new in neuroimaging, and it became evident years ago with the proliferation of publicly available neuroimaging datasets. To solve this critical issue, the neuroimaging community created the Brain Imaging Data Structure (BIDS) that specifies standards for how datasets should be organized to facilitate sharing and reproducibility of science. Currently, BIDS supports dozens of neuroimaging modalities including MRI, EEG, MEG, PET, and many others. In this paper, we present the extension of BIDS for NIRS data alongside tools that may assist researchers in organizing existing and new data with the goal of promoting public disseminations of fNIRS datasets.

Objective Neurophysiologic measures like the electrically evoked Stapedius Reflex Threshold (eSRT) offer valuable insights into personalized responses to Cochlear Implant (CI) stimuli. This study evaluates the effects of stimulation current amplitude and the number of stimulating electrodes on eSRT and its correlation with the Most Comfortable Loudness level (MCL or C-level) in all-polar, pseudo-monophasic, pulse-width loudness coded stimulations. Approach The study was conducted on seventeen adult patients with Oticon Medical implants. In these implants, current amplitude remains constant while loudness is coded through pulse-width. Stimulation amplitudes were set to low (default clinical) and high values, using single or multi electrode groups of 1, 3, and 5 (G1, G3, and G5) across five cochlear regions, spanning from apical to basal. For each amplitude and group, the eSRT detection rate and correlation with MCL were analyzed regionally and overall. Results Higher eSRT detection rates were observed in the contra-lateral ear, with G3 and G5 significantly enhancing detection over G1 for both low and high amplitudes. The current amplitude facilitated eSRT invocation, particularly for G1 and patients with higher MCLs. The correlation between eSRT and MCL was relatively high (r = 0.64–0.87) across all conditions, indicating accurate MCL estimation from eSRT at both low and high amplitudes. Discussions Among all condition interactions (amplitude, side, electrodes), low-amplitude contra-lateral G5 and high-amplitude contra-lateral G3 showed optimal modes for estimating MCL from eSRT, with linear slopes near 1.0, and detection rates of 54% and 63%, respectively. High-amplitude contra-lateral G5 further increased detection to 68%, though with a slope of 0.8, requiring a correction factor. These findings provide insights into eSRT mechanisms and its application in pseudo-monophasic all-polar CI programming. Conclusion The eSRT detection rate can be improved in pulse-width modulation cochlear implants by using multi-electrode stimulation and/or increasing the stimulation amplitude beyond the default clinical value, without significantly affecting the correlation coefficient between eSRT and MCL.

Although the existing literature has explored the link between cognitive functioning and speech recognition in noise, the specific role of fluid intelligence still needs to be studied. Given the established association between working memory capacity (WMC) and fluid intelligence and the predictive power of WMC for speech recognition in noise, we aimed to elucidate the mediating role of fluid intelligence. We used data from the n200 study, a longitudinal investigation into aging, hearing ability, and cognitive functioning. We analyzed two age-matched samples: participants with hearing aids and a group with normal hearing. WMC was assessed using the Reading Span task, and fluid intelligence was measured with Raven's Progressive Matrices. Speech recognition in noise was evaluated using Hagerman sentences presented to target 80% speech-reception thresholds in four-talker babble. Data were analyzed using mediation analysis to examine fluid intelligence as a mediator between WMC and speech recognition in noise. We found a partial mediating effect of fluid intelligence on the relationship between WMC and speech recognition in noise, and that hearing status did not moderate this effect. In other words, WMC and fluid intelligence were related, and fluid intelligence partially explained the influence of WMC on speech recognition in noise. This study shows the importance of fluid intelligence in speech recognition in noise, regardless of hearing status. Future research should use other advanced statistical techniques and explore various speech recognition tests and background maskers to deepen our understanding of the interplay between WMC and fluid intelligence in speech recognition.