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- Creators: Berisha, Visar
Description
The recent spotlight on concussion has illuminated deficits in the current standard of care with regard to addressing acute and persistent cognitive signs and symptoms of mild brain injury. This stems, in part, from the diffuse nature of the injury, which tends not to produce focal cognitive or behavioral deficits that are easily identified or tracked. Indeed it has been shown that patients with enduring symptoms have difficulty describing their problems; therefore, there is an urgent need for a sensitive measure of brain activity that corresponds with higher order cognitive processing. The development of a neurophysiological metric that maps to clinical resolution would inform decisions about diagnosis and prognosis, including the need for clinical intervention to address cognitive deficits. The literature suggests the need for assessment of concussion under cognitively demanding tasks. Here, a joint behavioral- high-density electroencephalography (EEG) paradigm was employed. This allows for the examination of cortical activity patterns during speech comprehension at various levels of degradation in a sentence verification task, imposing the need for higher-order cognitive processes. Eight participants with concussion listened to true-false sentences produced with either moderately to highly intelligible noise-vocoders. Behavioral data were simultaneously collected. The analysis of cortical activation patterns included 1) the examination of event-related potentials, including latency and source localization, and 2) measures of frequency spectra and associated power. Individual performance patterns were assessed during acute injury and a return visit several months following injury. Results demonstrate a combination of task-related electrophysiology measures correspond to changes in task performance during the course of recovery. Further, a discriminant function analysis suggests EEG measures are more sensitive than behavioral measures in distinguishing between individuals with concussion and healthy controls at both injury and recovery, suggesting the robustness of neurophysiological measures during a cognitively demanding task to both injury and persisting pathophysiology.
ContributorsUtianski, Rene (Author) / Liss, Julie M (Thesis advisor) / Berisha, Visar (Committee member) / Caviness, John N (Committee member) / Dorman, Michael (Committee member) / Arizona State University (Publisher)
Created2014
Description
The ability of cochlear implants (CI) to restore auditory function has advanced significantly in the past decade. Approximately 96,000 people in the United States benefit from these devices, which by the generation and transmission of electrical impulses, enable the brain to perceive sound. But due to the predominantly Western cochlear implant market, current CI characterization primarily focuses on improving the quality of American English. Only recently has research begun to evaluate CI performance using other languages such as Mandarin Chinese, which rely on distinct spectral characteristics not present in English. Mandarin, a tonal language utilizes four, distinct pitch patterns, which when voiced a syllable, conveys different meanings for the same word. This presents a challenge to hearing research as spectral, or frequency based information like pitch is readily acknowledged to be significantly reduced by CI processing algorithms. Thus the present study sought to identify the intelligibility differences for English and Mandarin when processed using current CI strategies. The objective of the study was to pinpoint any notable discrepancies in speech recognition, using voice-coded (vocoded) audio that simulates a CI generated stimuli. This approach allowed 12 normal hearing English speakers, and 9 normal hearing Mandarin listeners to participate in the experiment. The number of frequency channels available and the carrier type of excitation were varied in order to compare their effects on two cases of Mandarin intelligibility: Case 1) word recognition and Case 2) combined word and tone recognition. The results indicated a statistically significant difference between English and Mandarin intelligibility for Condition 1 (8Ch-Sinewave Carrier, p=0.022) given Case 1 and Condition 1 (8Ch-Sinewave Carrier, p=0.001) and Condition 3 (16Ch-Sinewave Carrier, p=0.001) given Case 2. The data suggests that the nature of the carrier type does have an effect on tonal language intelligibility and warrants further research as a design consideration for future cochlear implants.
ContributorsSchiltz, Jessica Hammitt (Author) / Berisha, Visar (Thesis director) / Frakes, David (Committee member) / Barrett, The Honors College (Contributor) / Harrington Bioengineering Program (Contributor)
Created2015-05
Description
Individuals with voice disorders experience challenges communicating daily. These challenges lead to a significant decrease in the quality of life for individuals with dysphonia. While voice amplification systems are often employed as a voice-assistive technology, individuals with voice disorders generally still experience difficulties being understood while using voice amplification systems. With the goal of developing systems that help improve the quality of life of individuals with dysphonia, this work outlines the landscape of voice-assistive technology, the inaccessibility of state-of-the-art voice-based technology and the need for the development of intelligibility improving voice-assistive technologies designed both with and for individuals with voice disorders. With the rise of voice-based technologies in society, in order for everyone to participate in the use of voice-based technologies individuals with voice disorders must be included in both the data that is used to train these systems and the design process. An important and necessary step towards the development of better voice assistive technology as well as more inclusive voice-based systems is the creation of a large, publicly available dataset of dysphonic speech. To this end, a web-based platform to crowdsource voice disorder speech was developed to create such a dataset. This dataset will be released so that it is freely and publicly available to stimulate research in the field of voice-assistive technologies. Future work includes building a robust intelligibility estimation model, as well as employing that model to measure, and therefore enhance, the intelligibility of a given utterance. The hope is that this model will lead to the development of voice-assistive technology using state-of-the-art machine learning models to help individuals with voice disorders be better understood.
ContributorsMoore, Meredith Kay (Author) / Panchanathan, Sethuraman (Thesis advisor) / Berisha, Visar (Committee member) / McDaniel, Troy (Committee member) / Venkateswara, Hemanth (Committee member) / Arizona State University (Publisher)
Created2020