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Spatial Neglect Detection and Field of View Estimation using a Novel Brain-Computer Interface

Kocanaogullari, Deniz (2021) Spatial Neglect Detection and Field of View Estimation using a Novel Brain-Computer Interface. Master's Thesis, University of Pittsburgh. (Unpublished)

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Spatial neglect (SN) is a neurological syndrome in stroke patients, commonly due to unilateral brain injury. It results in inattention to stimuli in the contralesional visual field. The current gold standard for SN assessment is the behavioral inattention test (BIT). BIT includes a series of pen-and-paper tests. These tests can be unreliable due to high variability in subtest performances; they are limited in their ability to measure the extent of neglect, and they do not assess the patients in a realistic and dynamic environment. In this thesis, we present an electroencephalography (EEG)-based brain-computer interface (BCI) that utilizes the Starry Night Test to overcome the limitations of the traditional SN assessment tests. Our first goal with the implementation of this EEG-based Starry Night neglect detection system is to provide a more detailed assessment of SN. Specifically, to detect the presence of SN and its severity. To achieve this goal, as an initial step, we utilize a convolutional neural network (CNN) based model to analyze EEG data and accordingly propose a neglect detection method to distinguish between stroke patients without neglect and stroke patients with neglect. In this project, we also propose an EEG-based BCI that utilizes an augmented reality (AR) headset to present the Starry Night Test to overcome the limitations of the BIT. The ultimate goal of this implementation is to provide a more nuanced assessment of SN on patients with stroke and also to overcome the limitations of computer screens by using an AR headset. As an initial step to achieve these goals, we use common spatial patterns (CSP) and create a classifier using linear discriminant analysis (LDA) to propose a field of view estimation method for stroke patients both with and without spatial neglect.


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Item Type: University of Pittsburgh ETD
Status: Unpublished
CreatorsEmailPitt UsernameORCID
Kocanaogullari, Denizdek107@pitt.edudek1070000-0003-0493-2297
ETD Committee:
TitleMemberEmail AddressPitt UsernameORCID
Committee ChairAkcakaya, Muratakcakaya@pitt.eduakcakaya
Committee MemberSejdic, Ervinesejdic@pitt.eduesejdic
Committee MemberMao, Zhi-Hongzhm4@pitt.eduzhm4
Date: 13 June 2021
Date Type: Publication
Defense Date: 2 April 2021
Approval Date: 13 June 2021
Submission Date: 8 April 2021
Access Restriction: No restriction; Release the ETD for access worldwide immediately.
Number of Pages: 50
Institution: University of Pittsburgh
Schools and Programs: Swanson School of Engineering > Electrical and Computer Engineering
Degree: MS - Master of Science
Thesis Type: Master's Thesis
Refereed: Yes
Uncontrolled Keywords: spatial neglect, brain-computer interface, augmented reality, neural networks, field of view estimation
Date Deposited: 13 Jun 2021 18:36
Last Modified: 13 Jun 2021 18:36


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