Author ORCID Identifier
Document Type
Dissertation
Date of Award
2024
Degree Name
Doctor of Philosophy (PhD)
Department
Psychology
First Advisor
Lee Baugh
Abstract
Every year, clinicians diagnose 5000 new Amyotrophic Lateral Sclerosis (ALS) cases in the United States (Mehta et al., 2018). ALS is a degenerative neuromuscular disease that prevents neurons from sending impulses to the muscles, thus resulting in paralysis and death. People with ALS (PALS) not only experience limited mobility but also lose their ability to communicate. Although the disease currently remains incurable, efforts to improve the patients’ communication are increasingly leading toward Augmentative and Alternative Communication (AAC) systems (Beukelman and Mirenda, 2013). AAC systems are assistive technologies that propose to counteract the defects resulting from ALS through non-verbal communication channels. Current AAC systems applied to ALS rely on Brain-Computer Interfaces (BCI), which, although effective, present significant limitations. Said limitations include BCI’s intrusiveness, overreliance on electrode placement, and inconsistent performance by individual users (Mak and Wolpaw, 2009). Therefore, it is incumbent on Human Factor Engineers to identify a means to mitigate these limitations, one that is less intrusive, independent of electrode placement, and promises a consistent and accurate performance by individual users. The present study proposes solutions to overcome this challenge by exploring the premises of a novel pupil-based AAC system. Such a strategy is achievable by applying BCI strategies to eye-tracking methods, namely pupillometry. The results of our analysis suggest that it is possible to develop a pupil-based system using BCI strategies such as the Rapid Serial Visual Presentation (oddball) paradigm and the Event-Related Synchronization /Desynchronization (motor imagery) paradigm.
Subject Categories
Cognitive Psychology | Computer Sciences | Neurosciences
Keywords
Amyotrophic Lateral Sclerosis (ALS), Brain Computer Interfaces (BCI), Event-Related Potentials (ERPs), Event-Related Synchronization/Desynchronization (ERSD), Pupillometry Visual Steady State Evoked Potential (VSSE)
Number of Pages
154
Publisher
University of South Dakota
Recommended Citation
Niamba, Kouadio Marc-Antoine, "Pupillometry as a Viable Augmentative and Alternative Communication Pathway: a Machine Learning Application" (2024). Dissertations and Theses. 291.
https://red.library.usd.edu/diss-thesis/291