Spatial Neglect Detection and Field of View Estimation using a Novel Brain-Computer Interface
Year: 2021
Type: dissertation
Abstract: 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.
Author: Deniz Kocanaogullari
Language: English
Subfield: Cognitive Neuroscience
Field: Neuroscience
Domain: Life Sciences
Open Access status: green
OpenAlex ID: W3167418856