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Psychophysics-Based Electrode Selection for Cochlear Implant Listeners

dc.contributor.advisor Collins, Leslie M Duran, Sara Ingrid 2014-05-14T19:17:35Z 2015-05-09T04:30:05Z 2014
dc.description.abstract <p>Cochlear implant listeners are presented with a time and frequency-quantized version of speech signals. In the frequency domain, resolution is limited by the number of electrodes in each listener's array. Current cochlear implant speech processing strategies implicitly assume that the information presented to each one of these electrodes is perceived as unique and independent. However, previous research suggests that stimuli presented on different electrodes can be indiscriminable (e.g. Zwolan et al., 1997; Throckmorton and Collins, 1999; Henry et al., 2000) . Additional studies suggest that stimuli presented on one electrode can influence the perception of stimuli on neighboring electrodes (e.g. Shannon, 1990; Chatterjee and Shannon, 1998; Boëx et al., 2003). Removing this redundant or occluded information could cause more distinct or perceivable information to be presented to the listener and possibly result in improved speech recognition.</p><p>Previous studies have used psychophysical data to identify the electrodes with the highest potential to confound speech recognition (Zwolan et al., 1997, Boëx et al., 2003, and Garadat et al., 2012). In order to minimize electrode interactions and maximize the amount of perceivable information, each of these studies used a single psychophysical metric to deactivate the electrodes across all time windows of the speech processing strategy. For some listeners, these reduced electrode sets resulted in improved speech recognition over using the of the electrodes in their array. These studies did not compare the results of using different psychophysical metrics to exclude electrodes for a group of listeners nor did they investigate speech recognition performance as a function of the number of electrodes excluded from the array.</p><p>In this work, three different psychophysical metrics were used to obtain a multidimensional estimate of the potential "usefulness'' of each electrode. These results were then used to inform two different methods of psychophysics-motivated electrode selection. The first method incorporated individual data into each listener's energy-driven speech processing strategy. For each time window, the electrodes with the highest energy that were also most likely to be perceived, according to the psychophysical data, were selected for stimulation. The second method sequentially excluded the electrodes with the highest potential to confound from the array across all time windows, resulting in a group of psychophysics-motivated electrode sets for each metric. Evaluating each of these electrode sets exhaustively would require a prohibitive amount of experimental time. To mitigate this problem, an adaptive procedure was developed to estimate performance as a function of cochlear implant parameters in a time-efficient manner. For each metric, the procedure estimated the set with the highest estimated probability of correct phoneme identification. Listeners' speech recognition performance using this electrode set was then compared to their performance using their full electrode array. For both electrode selection methods, listeners' speech recognition scores were generally comparable to those obtained in the clinical condition. This finding supports the hypothesis that listeners were not perceiving all the information presented to them using their clinical speech processing strategy and their complete set of electrodes. Additionally, these results suggest that improvements to the proposed electrode selection strategies should be in investigated in order to increase the amount of perceivable information presented to cochlear implant listeners.</p>
dc.subject Electrical engineering
dc.subject cochlear implants
dc.subject electrode selection
dc.subject psychophysics
dc.subject speech processing
dc.title Psychophysics-Based Electrode Selection for Cochlear Implant Listeners
dc.type Dissertation
dc.department Electrical and Computer Engineering
duke.embargo.months 12

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