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Unifying information theory and machine learning in a model of electrode discrimination in cochlear implants

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dc.contributor.authorGao, X.
dc.contributor.authorGrayden, D.
dc.contributor.authorMcDonnell, M.
dc.contributor.editorBuechner, A.
dc.date.issued2021
dc.descriptionData source: Supporting information, https://doi.org/10.1371/journal.pone.0257568
dc.description.abstractDespite the development and success of cochlear implants over several decades, wide inter-subject variability in speech perception is reported. This suggests that cochlear implant user-dependent factors limit speech perception at the individual level. Clinical studies have demonstrated the importance of the number, placement, and insertion depths of electrodes on speech recognition abilities. However, these do not account for all inter-subject variability and to what extent these factors affect speech recognition abilities has not been studied. In this paper, an information theoretic method and machine learning technique are unified in a model to investigate the extent to which key factors limit cochlear implant electrode discrimination. The framework uses a neural network classifier to predict which electrode is stimulated for a given simulated activation pattern of the auditory nerve, and mutual information is then estimated between the actual stimulated electrode and predicted ones. We also investigate how and to what extent the choices of parameters affect the performance of the model. The advantages of this framework include i) electrode discrimination ability is quantified using information theory, ii) it provides a flexible framework that may be used to investigate the key factors that limit the performance of cochlear implant users, and iii) it provides insights for future modeling studies of other types of neural prostheses.
dc.identifier.citationPLoS ONE, 2021; 16(9 September)
dc.identifier.doi10.1371/journal.pone.0257568
dc.identifier.issn1932-6203
dc.identifier.issn1932-6203
dc.identifier.orcidMcDonnell, M. [0000-0002-7009-3869]
dc.identifier.urihttps://hdl.handle.net/11541.2/25728
dc.language.isoen
dc.publisherPublic Library of Science (PLoS)
dc.rightsCopyright 2021 Gao et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. (https://creativecommons.org/licenses/by/4.0/)
dc.source.urihttps://doi.org/10.1371/journal.pone.0257568
dc.subjectcochlear implants
dc.subjecthearing loss
dc.subjectelectrode discrimination
dc.titleUnifying information theory and machine learning in a model of electrode discrimination in cochlear implants
dc.typeJournal article
pubs.publication-statusPublished
ror.fileinfo12235006160001831 13235006150001831 journal.pone.0257568
ror.mmsid9916570150201831

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