Biology subjects

Nogueira, W.

Publications and source records attributed to Nogueira, W..

3 recordsLinked to original sources

bioRxiv2021

An End-to-End Deep Learning Sound Coding Strategy for Cochlear Implants

Cochlear implant (CI) users struggle to understand speech in noisy conditions. To address this problem, we propose a deep learning speech denoising sound coding strategy that estimates the CI electric stimulation patterns out of the raw audio data captured by the micro-phone, performing end-to-end CI processing. To estimate the relative denoising performance differences between various approaches, we compared this technique to a classic Wiener filter and to a convTasNet. Speech enhancement performance was assessed by means of signal-to-noise-ratio improvement and the short-time objective speech intelligibility measure. Additionally, 5 CI users were evaluated for speech intelligibility in noise to assess the potential benefits of each algorithm. Our results show that the proposed method is capable of replacing a CI sound coding strategy while preserving its general use for every listener and performing speech enhancement in noisy environments, without sacrificing algorithmic latency.

bioengineering↗

bioRxiv2021

PREDICTING SPEECH INTELLIGIBILITY FROM A SELECTIVE ATTENTION DECODING PARADIGM IN COCHLEAR IMPLANT USERS

1.ObjectivesElectroencephalography (EEG) can be used to decode selective attention in cochlear implant (CI) users. This work investigates if selective attention to an attended speech source in the presence of a concurrent speech source can predict speech understanding in CI users. ApproachCI users were instructed to attend to one out of two speech streams while EEG was recorded. Both speech streams were presented to the same ear and at different signal to interference ratios (SIRs). Speech envelope reconstruction of the to-be-attended speech from EEG was obtained by training decoders using regularized least squares. The correlation coefficient between the reconstructed and the attended ({rho}ASIR) and between the reconstructed and the unattended ({rho}USIR) speech stream at each SIR was computed. Main ResultsSelective attention decoding in CI users is possible even if both speech streams are presented monaurally. A significant effect of SIR on the correlation coefficient to the attended signal{rho} ASIR, as well as on the difference correlation coefficients{rho} ASIR -{rho} USIR and{rho} ASIR -{rho} USIR was observed, but not on the unattended correlation coefficient{rho} USIR. Finally, the results show a significant correlation between speech understanding performance and the correlation coefficients{rho} ASIR-{rho}USIR or -{rho}USIR across subjects. Moreover, the difference correlation coefficient{rho} ASIR -{rho} U-SIR, which is less affected by the CI electrical artifact, presented a correlation trend with speech understanding performance. SignificanceSelective attention decoding in CI users is possible, however care needs to be taken with the CI artifact and the speech material used to train the decoders. Even if only a small correlation trend between selective attention decoding and speech understanding was observed, these results are important for future development of objective speech understanding measures for CI users.

bioengineering↗

bioRxiv2021

Towards Decoding Selective Attention Through Cochlear Implant Electrodes as Sensors in Subjects with Contralateral Acoustic Hearing

ObjectivesFocusing attention on one speaker in a situation with multiple background speakers or noise is referred to as auditory selective attention. Decoding selective attention is an interesting line of research with respect to future brain-guided hearing aids or cochlear implants (CIs) that are designed to adaptively adjust sound processing through cortical feedback loops. This study investigates the feasibility of using the electrodes and backward telemetry of a CI to record electroencephalography (EEG). ApproachThe study population included 6 normal-hearing (NH) listeners and 5 CI users with contralateral acoustic hearing. Cortical auditory evoked potentials (CAEP) and selective attention were recorded using a state-of-the-art high-density scalp EEG and, in the case of CI users, also using two CI electrodes as sensors in combination with the backward telemetry system of these devices (iEEG). Main resultsThe peak amplitudes of the CAEPs recorded with iEEG were lower and the latencies were higher than those recorded with scalp EEG. In the selective attention paradigm with multi-channel scalp EEG the mean decoding accuracy across subjects was 92.0 and 92.5% for NH listeners and CI users, respectively. With single-channel scalp EEG the accuracy decreased to 65.6 and to 75.8% for NH listeners and CI users, respectively, and was above chance level in 9 out of 11 subjects. With the single-channel iEEG, the accuracy for CI users decreased to 70% and was above chance level in 3 out of 5 subjects. SignificanceThis study shows that single-channel EEG is suitable for auditory selective attention decoding, even though it reduces the decoding quality compared to a multi-channel approach. CI-based iEEG can be used for the purpose of recording CAEPs and decoding selective attention. However, the study also points out the need for further technical development for the CI backward telemetry regarding long-term recordings and the optimal sensor positions.

neuroscience↗