Neural envelope tracking as a measure of speech understanding in cochlear implant users

ABSTRACTThe speech envelope is essential for speech understanding and can be reconstructed from the electroencephalogram (EEG) recorded while listening to running speech. This so-called neural envelope tracking has been shown to relate to speech understanding in normal hearing listeners, but has barely been investigated in persons wearing cochlear implants (CI). We investigated the relation between speech understanding and neural envelope tracking in CI users.EEG was recorded in 8 CI users while they listened to a story. Speech understanding was varied by changing the intensity of the presented speech. The speech envelope was reconstructed from the EEG using a linear decoder and then correlated with the envelope of the speech stimulus as a measure of neural envelope tracking which was compared to actual speech understanding.This study showed that neural envelope tracking increased with increasing speech understanding in every participant. Furthermore behaviorally measured speech understanding was correlated with participant specific neural envelope tracking results indicating the potential of neural envelope tracking as an objective measure of speech understanding in CI users. This could enable objective and automatic fitting of CIs and pave the way towards closed-loop CIs that adjust continuously and automatically to individual CI users.

A Simulated Annealing based Optimization Algorithm for Automatic Variogram Model Fitting

Fitting a theoretical model to an experimental variogram is an important issue in geostatistical studies because if the variogram model parameters are tainted with uncertainty, the latter will spread in the results of estimations and simulations. Although the most popular fitting method is fitting by eye, in some cases use is made of the automatic fitting method on the basis of putting together the geostatistical principles and optimization techniques to: 1) provide a basic model to improve fitting by eye, 2) fit a model to a large number of experimental variograms in a short time, and 3) incorporate the variogram related uncertainty in the model fitting. Effort has been made in this paper to improve the quality of the fitted model by improving the popular objective function (weighted least squares) in the automatic fitting. Also, since the variogram model function (£) and number of structures (m) too affect the model quality, a program has been provided in the MATLAB software that can present optimum nested variogram models using the simulated annealing method. Finally, to select the most desirable model from among the single/multi-structured fitted models, use has been made of the cross-validation method, and the best model has been introduced to the user as the output. In order to check the capability of the proposed objective function and the procedure, 3 case studies have been presented.