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

• Published in: PloS one Vol. 16; no. 9; p. e0257568
• Main Authors: Gao, Xiao, Grayden, David, McDonnell, Mark
• Format: Journal Article
• Language: English
• Published: San Francisco Public Library of Science 20.09.2021; Public Library of Science (PLoS)
• Subjects: Analysis; Auditory discrimination; Auditory nerve; Biology and Life Sciences; Biomedical engineering; Cochlea; Cochlear implants; Complications and side effects; Computer and Information Sciences; Discrimination; Electrodes; Engineering and Technology; Evaluation; Geometry; Information theory; Learning algorithms; Machine learning; Medicine and Health Sciences; Modelling; Neural networks; Neural prostheses; Patient outcomes; Perception; Physical Sciences; Prosthetics; Random variables; Speech; Speech perception; Speech recognition; Transplants & implants; Voice recognition; Australia
• DOI: 10.1371/journal.pone.0257568
• ISSN: 1932-6203

Despite 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.