Auditory processing in general and music perception in particular are hampered in adult Cochlear Implant (CI) users. To examine the residual music perception skills and their underlying neural correlates in CI users implanted in adolescence or adulthood, we conducted an electrophysiological and behavioural study comparing adult CI users with normal-hearing age-matched controls (NH controls). We used a newly developed musical multi-feature paradigm, which makes it possible to test automatic auditory discrimination of six different types of sound feature changes inserted within a musical enriched setting lasting only 20 minutes. The presentation of stimuli did not require the participants’ attention, allowing the study of the early automatic stage of feature processing in the auditory cortex. For the CI users, we obtained mismatch negativity (MMN) brain responses to five feature changes but not to changes of rhythm, whereas we obtained MMNs for all the feature changes in the NH controls. Furthermore, the MMNs to Deviants of pitch of CI users were reduced in amplitude and later than those of NH controls for changes of pitch and guitar timbre. No other group differences in MMN parameters were found to changes in intensity and saxophone timbre. Furthermore, the MMNs in CI users reflected the behavioral scores from a respective discrimination task and were correlated with patients’ age and speech intelligibility. Our results suggest that even though CI users are not performing at the same level as NH controls in neural discrimination of pitch-based features, they do possess potential neural abilities for music processing. However, CI users showed a disrupted ability to automatically discriminate rhythmic changes compared with controls. The current behavioural and MMN findings highlight the residual neural skills for music processing even in CI users who have been implanted in adolescence or adulthood.

The robust delineation of the cochlea and its inner structures combined with the detection of the electrode of a cochlear implant within these structures is essential for envisaging a safer, more individualized, routine image-guided cochlear implant therapy. We present Nautilus—a web-based research platform for automated pre- and post-implantation cochlear analysis. Nautilus delineates cochlear structures from pre-operative clinical CT images by combining deep learning and Bayesian inference approaches. It enables the extraction of electrode locations from a post-operative CT image using convolutional neural networks and geometrical inference. By fusing pre- and post-operative images, Nautilus is able to provide a set of personalized pre- and post-operative metrics that can serve the exploration of clinically relevant questions in cochlear implantation therapy. In addition, Nautilus embeds a self-assessment module providing a confidence rating on the outputs of its pipeline. We present a detailed accuracy and robustness analyses of the tool on a carefully designed dataset. The results of these analyses provide legitimate grounds for envisaging the implementation of image-guided cochlear implant practices into routine clinical workflows.

Access to low-frequency acoustic information in cochlear implant patients leads to better speech understanding in noise. Electrocochleography (ECochG) can provide real-time feedback about the health of the cochlea during the insertion process with the potential to reduce insertion trauma. We describe our experiences of using this technique. Data from 47 adult subjects with measurable residual hearing and an Advanced Bionics (Valencia, CA) SlimJ (46) or MidScala (1) electrode array were analyzed. ECochGs were recorded intraoperatively via the implant. The surgeon adjusted the course of the electrode insertion based on drops in the ECochG. The final array position was assessed using postoperative imaging and pure tone thresholds were measured before and after surgery. Three different patterns of ECochG response amplitude were observed: Growth, Fluctuating and Total Loss. Subjects in the growth group showed the smallest postoperative hearing loss. However, the group with fluctuating amplitudes showed no meaningful correlation between the ECochG responses and the postoperative hearing loss, indicating that amplitude alone is insufficient for detecting damage. Considering the phase of the signal additionally to the amplitude and reclassifying the data by both the phase and amplitude of the response into three groups Type I–Type III produced statistically significant correlations between postoperative hearing loss and the grouping based on amplitude and phase respectively. We showed significantly better hearing preservation for Type I (no drop in amplitude) and Type II (drop with a concurrent phase shift), while Type III (drop without concurrent phase shift) had more surgery induced hearing loss. ECochG potentials measured through the implant could provide valuable feedback during the electrode insertion. Both the amplitude and phase of the ECochG response are important to consider. More data needs to be evaluated to better understand the impact of the different signal components to design an automated system to alert the surgeon ahead of damaging the cochlea.

Previous results showed that it is possible to decode an attended speech source from EEG data via the reconstruction of the speech envelope in normal hearing (NH) listeners. However, so far it is unknown how the performance of such a decoder is affected by the decrease in spectral resolution and the electrical artifacts introduced by a cochlear implant (CI) in users of these prostheses. NH-listeners and bilateral CI-users participated in the present study. Speech from two audio books, one uttered by a male voice and one by a female voice, was presented to NH-listeners and CI-users. Participants were instructed to attend to one of the two speech streams presented dichotically while a 96- channel EEG was recorded. Speech envelope reconstruction from the EEG data was obtained by training decoders using a regularized least square estimation method. Decoding accuracy was defined as the percentage of accurately reconstructed trials for each subject. For NH listeners, the experiment was repeated using a vocoder to reduce spectral-resolution and to stimulate speech perception with a CI in NH-listeners. The results showed a decoding accuracy of 80.9% using the original sound files in NH-listeners. The performance dropped to 73.3% in the vocoder condition and to 71.2% in the group of CI-users. In sum, although the accuracy drops when the spectral resolution becomes worse, the results show the feasibility to decode the attended sound source in NH-listeners with a vocoder simulation, and even in CI-users albeit more training data are needed.