Deactivating cochlear implant electrodes to improve speech perception: A computational approach

A potential bottleneck to improving speech perception performance in cochlear implant (CI) users is that some of their electrodes may poorly encode speech information. Several studies have examined the effect of deactivating poorly encoding electrodes on speech perception with mixed results. Many of these studies focused on identifying poorly encoding electrodes by some measure (e.g. electrode discrimination, pitch ordering, threshold, CT-guided, masked modulation detection), but provide inconsistent criteria about which electrodes, and how many, should be deactivated, and without considering how speech information becomes distributed across the electrode array. The present simulation study addresses this issue using computational approaches. Previously validated models were used to generate predictions of speech scores as a function of all possible combinations of active electrodes in a 22-electrode array in three groups of hypothetical subjects representative of relatively better, moderate, and poorer performing CI users. Using high-performance computing, over 500 million predictions were generated. Although deactivation of the poorest encoding electrodes sometimes resulted in predicted benefit, this benefit was significantly less relative to predictions resulting from model-optimized deactivations. This trend persisted when using novel stimuli (i.e. other than those used for optimization) and when using different processing strategies. Optimum electrode deactivation patterns produced an average predicted increase in word scores of 10% with some scores increasing by more than 20%. Optimum electrode deactivation patterns typically included 11 to 19 (out of 22) active electrodes, depending on the performance group. Optimal active electrode combinations were those that maximized discrimination of speech cues, maintaining 80%-100% of the physical span of the array. The present study demonstrates the potential for further improving CI users' speech scores with appropriate selection of active electrodes.