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The New Jersey Institute of Technology's
Electronic Theses & Dissertations Project

Title: Denoising techniques reveal neural correlates of modulation masking release in auditory cortex
Author: Chaubal, Sahil
View Online: njit-etd2017-022
(xiii, 67 pages ~ 4.0 MB pdf)
Department: Department of Biomedical Engineering
Degree: Master of Science
Program: Biomedical Engineering
Document Type: Thesis
Advisory Committee: Ihlefeld, Antje (Committee chair)
Foulds, Richard A. (Committee member)
Sahin, Mesut (Committee member)
Li, Xiaobo (Committee member)
Date: 2017-01
Keywords: Modulation masking release
Denoising algorithms
Availability: Unrestricted
Abstract:

Hearing aids allow hearing impaired (HI) individuals to regain auditory perception in quiet settings. However, despite advances in hearing aid technology, HI individuals do not perform as well in situations with background sound as normally hearing (NH) listeners. An extensive literature demonstrates that when comparing tone detection performance in background noise, NH listeners have better thresholds when that noise is temporally modulated as compared to temporally unmodulated. However, this perceptual benefit, called Modulation Masking Release (MMR), is much reduced in HI listeners, and this is thought to be a reason for why HI listeners struggle in the presence of background sound.

This study explores neural correlates of MMR in NH and HI gerbils. Trained, awake gerbils (Meriones unguiculatus) listen passively to a target tone (1 kHz) embedded in modulated or unmodulated noise while a 16-channel microelectrode array records multi-unit neural spike activity in core auditory cortex. In addition, microelectrodes also record nuisance signals due to animal movements and interference in the wireless recording setup. The current study examines the potency of three different denoising algorithms using signal detection theory. The first, amplitude rejection (AR) classifies events based on amplitude. The second, virtual referencing (VR) applies subtraction of a virtual common ground signal. The third, inter-electrode correlation (IEC) compares events across electrodes to decide whether to classify an event as noise or as spike. Using Receiver-Operator-Characteristics (ROC), these classifiers were ranked. Results suggest that combining IEC and VR leads to best denoising performance. Denoised spike train reveals a robust correlation of spike rate with behavioral performance. Results hint that neural correlates of MMR are not primarily based on spike rate coding, at least in the core auditory cortex.


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