Single- and multi-channel modulation detection in cochlear implant users.

John J Galvin,Deniz Başkent,Sandy Oba,Qian-Jie Fu,Manuel S Malmierca

doi:10.1371/journal.pone.0099338

Abstract

Single-channel modulation detection thresholds (MDTs) have been shown to predict cochlear implant (CI) users’ speech performance. However, little is known about multi-channel modulation sensitivity. Two factors likely contribute to multichannel modulation sensitivity: multichannel loudness summation and the across-site variance in single-channel MDTs. In this study, single- and multi-channel MDTs were measured in 9 CI users at relatively low and high presentation levels and modulation frequencies. Single-channel MDTs were measured at widely spaced electrode locations, and these same channels were used for the multichannel stimuli. Multichannel MDTs were measured twice, with and without adjustment for multichannel loudness summation (i.e., at the same loudness as for the single-channel MDTs or louder). Results showed that the effect of presentation level and modulation frequency were similar for single- and multi-channel MDTs. Multichannel MDTs were significantly poorer than single-channel MDTs when the current levels of the multichannel stimuli were reduced to match the loudness of the single-channel stimuli. This suggests that, at equal loudness, single-channel measures may over-estimate CI users’ multichannel modulation sensitivity. At equal loudness, there was no significant correlation between the amount of multichannel loudness summation and the deficit in multichannel MDTs, relative to the average single-channel MDT. With no loudness compensation, multichannel MDTs were significantly better than the best single-channel MDT. The across-site variance in single-channel MDTs varied substantially across subjects. However, the across-site variance was not correlated with the multichannel advantage over the best single channel. This suggests that CI listeners combined envelope information across channels instead of attending to the best channel.

Highlights

Temporal amplitude modulation (AM) detection is one of the few psychophysical measures that have been shown to predict speech perception by users of cochlear implants (CIs) [1,2] or auditory brainstem implants [3]
Various stimulation parameters have been shown to affect modulation detection thresholds (MDTs) measured on a single electrode, including current level, modulation frequency, and stimulation rate [2], [4,5,6,7,8,9,10,11,12,13,14]. In these single-channel modulation detection studies, MDTs generally improve as the current level is increased and as the modulation frequency is reduced
Chatterjee and colleagues [15], [18] measured modulation detection interference (MDI) by fluctuating maskers in CI subjects. They found significant MDI, even when the maskers were spatially remote from the target, suggesting that CI users combined temporal information across distant neural populations

Summary

Introduction

Temporal amplitude modulation (AM) detection is one of the few psychophysical measures that have been shown to predict speech perception by users of cochlear implants (CIs) [1,2] or auditory brainstem implants [3]. Because MDTs are level-dependent [4], [6], [8,9,10], [15], modulation sensitivity on individual channels may be poorer after adjusting for multichannel loudness summation Another factor that may affect multichannel temporal processing is acrosssite variability in single-channel modulation sensitivity. Chatterjee and colleagues [15], [18] measured modulation detection interference (MDI) by fluctuating maskers in CI subjects They found significant MDI, even when the maskers were spatially remote from the target, suggesting that CI users combined temporal information across distant neural populations (i.e., more central processing of temporal envelope information). To explore the effects of loudness summation on multichannel modulation sensitivity, multichannel MDTs were measured with and without adjustment for multichannel loudness summation

Methods

Results

Findings

Discussion

Conclusions