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      Event-Related Potentials of Single-Sided Deaf Cochlear Implant Users: Using a Semantic Oddball Paradigm in Noise

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          Abstract

          Introduction

          In individuals with single-sided deafness (SSD), who are characterised by profound hearing loss in one ear and normal hearing in the contralateral ear, binaural input is no longer present. A cochlear implant (CI) can restore functional hearing in the profoundly deaf ear, with previous literature demonstrating improvements in speech-in-noise intelligibility with the CI. However, we currently have limited understanding of the neural processes involved (e.g., how the brain integrates the electrical signal produced by the CI with the acoustic signal produced by the normal hearing ear) and how modulation of these processes with a CI contributes to improved speech-in-noise intelligibility. Using a semantic oddball paradigm presented in the presence of background noise, this study aims to investigate how the provision of CI impacts speech-in-noise perception of SSD-CI users.

          Method

          Task performance (reaction time, reaction time variability, target accuracy, subjective listening effort) and high density electroencephalography from twelve SSD-CI participants were recorded, while they completed a semantic acoustic oddball task. Reaction time was defined as the time taken for a participant to press the response button after stimulus onset. All participants completed the oddball task in three different free-field conditions with the speech and noise coming from different speakers. The three tasks were: (1) CI-On in background noise, (2) CI-Off in background noise, and (3) CI-On without background noise (Control). Task performance and electroencephalography data (N2N4 and P3b) were recorded for each condition. Speech in noise and sound localisation ability were also measured.

          Results

          Reaction time was significantly different between all tasks with CI-On ( M [ SE] = 809 [39.9] ms) having faster RTs than CI-Off ( M [ SE] = 845 [39.9] ms) and Control ( M [ SE] = 785 [39.9] ms) being the fastest condition. The Control condition exhibited significantly shorter N2N4 and P3b area latency compared to the other two conditions. However, despite these differences noticed in RTs and area latency, we observed similar results between all three conditions for N2N4 and P3b difference area.

          Conclusion

          The inconsistency between the behavioural and neural results suggests that EEG may not be a reliable measure of cognitive effort. This rationale is further supported by different explanations used in past studies to explain N2N4 and P3b effects. Future studies should look to alternative measures of auditory processing (e.g., pupillometry) to gain a deeper understanding of the underlying auditory processes that facilitate speech-in-noise intelligibility.

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          Most cited references56

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          EEGLAB: an open source toolbox for analysis of single-trial EEG dynamics including independent component analysis

          We have developed a toolbox and graphic user interface, EEGLAB, running under the crossplatform MATLAB environment (The Mathworks, Inc.) for processing collections of single-trial and/or averaged EEG data of any number of channels. Available functions include EEG data, channel and event information importing, data visualization (scrolling, scalp map and dipole model plotting, plus multi-trial ERP-image plots), preprocessing (including artifact rejection, filtering, epoch selection, and averaging), independent component analysis (ICA) and time/frequency decompositions including channel and component cross-coherence supported by bootstrap statistical methods based on data resampling. EEGLAB functions are organized into three layers. Top-layer functions allow users to interact with the data through the graphic interface without needing to use MATLAB syntax. Menu options allow users to tune the behavior of EEGLAB to available memory. Middle-layer functions allow users to customize data processing using command history and interactive 'pop' functions. Experienced MATLAB users can use EEGLAB data structures and stand-alone signal processing functions to write custom and/or batch analysis scripts. Extensive function help and tutorial information are included. A 'plug-in' facility allows easy incorporation of new EEG modules into the main menu. EEGLAB is freely available (http://www.sccn.ucsd.edu/eeglab/) under the GNU public license for noncommercial use and open source development, together with sample data, user tutorial and extensive documentation.
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            Updating P300: an integrative theory of P3a and P3b.

            The empirical and theoretical development of the P300 event-related brain potential (ERP) is reviewed by considering factors that contribute to its amplitude, latency, and general characteristics. The neuropsychological origins of the P3a and P3b subcomponents are detailed, and how target/standard discrimination difficulty modulates scalp topography is discussed. The neural loci of P3a and P3b generation are outlined, and a cognitive model is proffered: P3a originates from stimulus-driven frontal attention mechanisms during task processing, whereas P3b originates from temporal-parietal activity associated with attention and appears related to subsequent memory processing. Neurotransmitter actions associating P3a to frontal/dopaminergic and P3b to parietal/norepinephrine pathways are highlighted. Neuroinhibition is suggested as an overarching theoretical mechanism for P300, which is elicited when stimulus detection engages memory operations.
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              The PREP pipeline: standardized preprocessing for large-scale EEG analysis

              The technology to collect brain imaging and physiological measures has become portable and ubiquitous, opening the possibility of large-scale analysis of real-world human imaging. By its nature, such data is large and complex, making automated processing essential. This paper shows how lack of attention to the very early stages of an EEG preprocessing pipeline can reduce the signal-to-noise ratio and introduce unwanted artifacts into the data, particularly for computations done in single precision. We demonstrate that ordinary average referencing improves the signal-to-noise ratio, but that noisy channels can contaminate the results. We also show that identification of noisy channels depends on the reference and examine the complex interaction of filtering, noisy channel identification, and referencing. We introduce a multi-stage robust referencing scheme to deal with the noisy channel-reference interaction. We propose a standardized early-stage EEG processing pipeline (PREP) and discuss the application of the pipeline to more than 600 EEG datasets. The pipeline includes an automatically generated report for each dataset processed. Users can download the PREP pipeline as a freely available MATLAB library from http://eegstudy.org/prepcode.
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                Author and article information

                Journal
                Audiol Neurootol
                Audiol Neurootol
                AUD
                AUD
                Audiology & Neurotology
                S. Karger AG (Basel, Switzerland )
                1420-3030
                1421-9700
                20 March 2023
                August 2023
                : 28
                : 4
                : 280-293
                Affiliations
                [a ]Division of Surgery, Medical School, The University of Western Australia, Perth, WA, Australia
                [b ]Department of Audiology, Fiona Stanley Fremantle Hospitals Group, Perth, WA, Australia
                [c ]School of Population Health, Curtin University, Perth, WA, Australia
                [d ]Deptartment of Otolaryngology, Head and Neck Surgery, Luzerner Kantonsspital, Luzern, Switzerland
                Author notes
                Correspondence to: Marcus Voola, marcusvoola@ 123456gmail.com
                Article
                529485
                10.1159/000529485
                10413801
                36940674
                4c33ef9c-43c2-4cf2-bc5d-90255b0ae390
                © 2023 The Author(s). Published by S. Karger AG, Basel

                This article is licensed under the Creative Commons Attribution-NonCommercial 4.0 International License (CC BY-NC) ( http://www.karger.com/Services/OpenAccessLicense). Usage and distribution for commercial purposes requires written permission.

                History
                : 22 June 2022
                : 31 January 2023
                : 2023
                Page count
                Figures: 7, Tables: 1, References: 56, Pages: 14
                Funding
                This research was partially supported by the Australian Government through the Australian Research Council’s Discovery Projects funding scheme (project DP180100394) awarded to WM. This project did not receive any other specific grant from funding agencies in the public, commercial, or not-for-profit sectors.
                DT awarded the research fellowship grant from the Rayne Medical Research Foundation. https://www.rainefoundation.org.au/research/funding-opportunities/clinician-research-fellowships/. The funders did not play any role in the study design, data collection, analysis, preparation of the manuscript, or decision to publish.
                Categories
                Research Article

                Audiology
                cochlear implants,event-related potentials,oddball,single-sided deafness
                Audiology
                cochlear implants, event-related potentials, oddball, single-sided deafness

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