Department of Biomedical Engineering, University of Melbourne, Parkville, VIC, Australia.
School of Physics, The University of Sydney, Sydney, NSW, Australia.
PLoS One. 2021 Sep 20;16(9):e0257568. doi: 10.1371/journal.pone.0257568. eCollection 2021.
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.
尽管在过去几十年中，人工耳蜗取得了发展和成功，但仍有报道称其在言语感知方面存在广泛的个体间差异。这表明，人工耳蜗使用者的个体差异限制了言语感知的个体水平。临床研究已经证明了电极数量、位置和插入深度对言语识别能力的重要性。然而，这些并不能解释所有的个体间差异，也不知道这些因素在多大程度上影响了言语识别能力。在本文中，我们将信息论方法和机器学习技术统一在一个模型中，以研究关键因素在多大程度上限制了人工耳蜗电极的辨别能力。该框架使用神经网络分类器来预测给定的听神经模拟激活模式下哪个电极被刺激，然后估计实际刺激电极和预测电极之间的互信息。我们还研究了参数选择如何以及在多大程度上影响模型的性能。该框架的优点包括：i）使用信息论量化电极辨别能力，ii）提供了一个灵活的框架，可用于研究限制人工耳蜗使用者性能的关键因素，iii）为其他类型的神经假体的未来建模研究提供了见解。
