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      Auditory Evoked Potential Response and Hearing Loss: A Review

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          There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.

          Abstract

          Hypoacusis is the most prevalent sensory disability in the world and consequently, it can lead to impede speech in human beings. One best approach to tackle this issue is to conduct early and effective hearing screening test using Electroencephalogram (EEG). EEG based hearing threshold level determination is most suitable for persons who lack verbal communication and behavioral response to sound stimulation. Auditory evoked potential (AEP) is a type of EEG signal emanated from the brain scalp by an acoustical stimulus. The goal of this review is to assess the current state of knowledge in estimating the hearing threshold levels based on AEP response. AEP response reflects the auditory ability level of an individual. An intelligent hearing perception level system enables to examine and determine the functional integrity of the auditory system. Systematic evaluation of EEG based hearing perception level system predicting the hearing loss in newborns, infants and multiple handicaps will be a priority of interest for future research.

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          The mismatch negativity (MMN) in basic research of central auditory processing: a review.

          R Näätänen, P Paavilainen, T Rinne … (2007)
          In the present article, the basic research using the mismatch negativity (MMN) and analogous results obtained by using the magnetoencephalography (MEG) and other brain-imaging technologies is reviewed. This response is elicited by any discriminable change in auditory stimulation but recent studies extended the notion of the MMN even to higher-order cognitive processes such as those involving grammar and semantic meaning. Moreover, MMN data also show the presence of automatic intelligent processes such as stimulus anticipation at the level of auditory cortex. In addition, the MMN enables one to establish the brain processes underlying the initiation of attention switch to, conscious perception of, sound change in an unattended stimulus stream.
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            The concept of auditory stimulus representation in cognitive neuroscience.

            R Näätänen, I Winkler (1999)
            The sequence of neurophysiological processes elicited in the auditory system by a sound is analyzed in search of the stage at which the processes carrying sensory information cross the borderline beyond which they directly underlie sound perception. Neurophysiological data suggest that this transition occurs when the sensory input is mapped onto the physiological basis of sensory memory in the auditory cortex. At this point, the sensory information carried by the stimulus-elicited process corresponds, for the first time, to that contained by the actual sound percept. Before this stage, the sensory stimulus code is fragmentary, lacks the time dimension, cannot enter conscious perception, and is not accessible to top-down processes (voluntary mental operations). On these grounds, 2 distinct stages of auditory sensory processing, prerepresentational and representational, can be distinguished.
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              EEG signal analysis: a survey.

              Suresh K. Joseph, D Subha, Ki Young Lim … (2010)
              The EEG (Electroencephalogram) signal indicates the electrical activity of the brain. They are highly random in nature and may contain useful information about the brain state. However, it is very difficult to get useful information from these signals directly in the time domain just by observing them. They are basically non-linear and nonstationary in nature. Hence, important features can be extracted for the diagnosis of different diseases using advanced signal processing techniques. In this paper the effect of different events on the EEG signal, and different signal processing methods used to extract the hidden information from the signal are discussed in detail. Linear, Frequency domain, time - frequency and non-linear techniques like correlation dimension (CD), largest Lyapunov exponent (LLE), Hurst exponent (H), different entropies, fractal dimension(FD), Higher Order Spectra (HOS), phase space plots and recurrence plots are discussed in detail using a typical normal EEG signal.
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                Author and article information

                Journal
                Journal ID (nlm-ta): Open Biomed Eng J
                Journal ID (iso-abbrev): Open Biomed Eng J
                Journal ID (publisher-id): TOBEJ
                Title: The Open Biomedical Engineering Journal
                Publisher: Bentham Open
                ISSN (Electronic): 1874-1207
                Publication date (Electronic): 27 February 2015
                Publication date Collection: 2015
                Volume: 9
                Pages: 17-24
                Affiliations
                [1 ]Department of Mechatronic Engineering, Universiti Malaysia Perlis, Malaysia
                [2 ]Faculty of Engineering, Karpagam University, India
                Author notes
                [* ]Address correspondence to this author at the The School of Mechatronic Engineering, Universiti Malaysia Perlis, Malaysia; Tel: +60164353674; Fax: +6049885167; E-mail: kamalrajecegmail.com; kamalrajece@ 123456yahoo.co.in
                Article
                Publisher ID: TOBEJ-9-17
                DOI: 10.2174/1874120701509010017
                PMC ID: 4391208
                PubMed ID: 25893012
                SO-VID: 78b65ae4-9889-47fa-af4f-aecec0526814
                Copyright © © Paulraj et al.; Licensee Bentham Open.
                License:

                This is an open access article licensed under the terms of the Creative Commons Attribution Non-Commercial License ( http://creativecommons.org/licenses/by-nc/3.0/) which permits unrestricted, non-commercial use, distribution and reproduction in any medium, provided the work is properly cited.

                History
                Date received : 12 July 2014
                Date revision received : 13 September 2014
                Date accepted : 18 September 2014
                Categories
                Subject: Article

                ScienceOpen disciplines: Biomedical engineering
                Keywords: auditory brainstem response,auditory evoked potential,electroencephalogram,hearing loss,hearing perception,neural networks
                Data availability:
                ScienceOpen disciplines: Biomedical engineering
                Keywords: auditory brainstem response, auditory evoked potential, electroencephalogram, hearing loss, hearing perception, neural networks

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