# A computational approach to optimal deactivation of cochlear implant electrodes

> **NIH NIH R21** · NEW YORK UNIVERSITY SCHOOL OF MEDICINE · 2022 · $211,875

## Abstract

PROJECT SUMMARY
Cochlear implantation has successfully restored hearing to hundreds of thousands of individuals worldwide.
However, speech understanding performance with CIs remains highly variable. Despite this variability in
outcomes, CIs are typically programmed with default settings with only a few subject-specific programming
parameters adjusted. This likely occurs because too many possible CI device setting combinations are
available and data-driven guidance to achieve maximal benefit for an individual CI user is lacking. For
example, most CIs are programmed with all viable electrodes active even though some electrodes may be
poorly suited to convey speech information. Previous studies evaluated whether selective deactivation of some
of these electrodes improved speech understanding, with limited success. However, these studies did not
consider how speech information is represented across remaining electrodes, or the optimal number of
electrodes to deactivate. The goal of the present study is to use computationally driven models of speech
understanding in CI users to guide the search for which combination of active electrodes can yield the best
speech understanding for a specific patient.
Aim 1 is to quantify speech understanding and sound quality with model-recommended combinations of active
electrodes compared to clinical standard-of-care settings, and compared to two control active electrode
combinations. These alternative conditions will use the same number of electrodes as the model-
recommended condition, but with electrodes selected in a similar way as previous studies. Subjects will have
1.5 months of regular use with each experimental active electrode conditions. Performance with experimental
and clinical active electrode conditions will be compared using a repeated-measures design. It is hypothesized
that the model-recommended condition will result in significantly better speech understanding than the other
conditions. Aim 2 is to translate the model-driven recommendations from Aim 1 into practical guidance about
how many (and possibly which) CI electrodes to deactivate. Subjects’ performance with the experimental and
clinical active electrode conditions of Aim 1 will be used to build a hierarchical linear model. This model will
relate performance with experimental active electrode settings to the following independent variables: number
of active electrodes selected for subjects by the model, physical span of active electrodes, subjects’ speech-
cue resolution with each experimental condition, and three demographic variables. It is hypothesized that
subjects’ performance will be more strongly correlated with the number of active electrodes and subjects’
speech-cue resolution, and moderately correlated to physical span of active electrodes.
Data obtained under the auspices of the current proposal will be foundational for larger studies to provide data-
driven guidance for optimal fitting of CI devices.

## Key facts

- **NIH application ID:** 10430378
- **Project number:** 1R21DC020293-01
- **Recipient organization:** NEW YORK UNIVERSITY SCHOOL OF MEDICINE
- **Principal Investigator:** Elad Sagi
- **Activity code:** R21 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2022
- **Award amount:** $211,875
- **Award type:** 1
- **Project period:** 2022-05-01 → 2024-04-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10430378

## Citation

> US National Institutes of Health, RePORTER application 10430378, A computational approach to optimal deactivation of cochlear implant electrodes (1R21DC020293-01). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/10430378. Licensed CC0.

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