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Author Spotlight: Optimizing EAS with Long Electrodes for Enhanced Cochlear Coverage and Hearing Preservation
Published on: October 11, 2024
Electro-Tactile Stimulation Enhances Cochlear Implant Speech Recognition in Noise
Juan Huang1, Benjamin Sheffield2,3, Payton Lin4
1Mind and Brain Institute, Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, USA.
Electro-tactile stimulation (ETS) enhances cochlear implant performance by converting low-frequency sounds into tactile vibrations. This novel approach improved speech recognition in noise for most users, offering a new avenue for hearing restoration.
Area of Science:
- Audiology
- Neuroscience
- Biomedical Engineering
Background:
- Combined electro-acoustic stimulation (EAS) improves cochlear implant (CI) performance.
- Many CI users lack residual low-frequency acoustic hearing.
- Tactile sensation operates in low frequencies (<500 Hz), similar to acoustic hearing in EAS.
Purpose of the Study:
- To investigate the efficacy of electro-tactile stimulation (ETS) as a method to enhance CI performance.
- To compare speech recognition in noise with CI alone versus bimodal ETS.
Main Methods:
- Ten cochlear implant users participated.
- A tactile aid converted voice fundamental frequency to vibrations on the index finger.
- Speech recognition in noise was tested under two conditions: CI alone and bimodal ETS.
Main Results:
- ETS improved speech reception thresholds by an average of 2.2 dB compared to CI alone.
- Nine out of ten subjects demonstrated a positive ETS effect, ranging from 0.3 to 7.0 dB.
- The magnitude of the ETS benefit was comparable to previously reported EAS benefits.
Conclusions:
- ETS is a viable method for enhancing cochlear implant performance, particularly for individuals with limited residual hearing.
- The comparable results suggest shared neural mechanisms underlying ETS and EAS.
- Complementary auditory and tactile sensory inputs show potential for improving speech perception in CI users and potentially in normal-hearing listeners and machine-based speech recognition.
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