Date of Award
11-1-2023
Language
English
Document Type
Master's Thesis
Degree Name
Master of Science (MS)
College/School/Department
Department of Electrical and Computer Engineering
Dissertation/Thesis Chair
James Norton
Committee Members
Gary Saulnier, Hany Elgala
Subject Categories
Biology
Abstract
This thesis describes work to design, optimize, and perform initial validation of an enhancedbrain-computer interface (BCI)-based color vision (CV) assessment protocol. Our overall goal was to enhance the speed, precision, and interpretability of BCI-based CV assessment. We drew inspiration from the design of the anomaloscope, the “gold-standard” of CV assessment, and eliminated differences in luminance between test stimuli that require existing BCI-based CV assessment protocols to complete a multidimensional search during BCI-based CV assessments. Following the protocol design phase, we completed three pilot experiments: (1) We analyzed the effect of ambient lighting on the new protocol; (2) We compared the results of our protocol using four different stimulation frequencies; and (3) We tested whether stimuli order affected the results of BCI-based CV assessment. After the pilot experiments, we used our enhanced protocol to collect data from seven participants who self-reported no history of color vision deficiencies (CVDs) and one person who self-reported a CVD. Results show differences in the behavioral and BCI-based CV assessments for the group of people who self-reported no CVDs compared with the individual with a self-reported CVD. Thus, even without further refinement, our protocol can distinguish between people with and without CVDs. In addition, our new protocol requires 1/3 less trials and each trial only lasts 60% as long. The work described in this thesis has enhanced BCI-based CV assessment and improved our understanding of multiple factors that affect BCI-based CV assessment. With further testing, the protocol will enable detection of congenital and acquired CVDs, even in people who are young or are unable to complete behavior-based CV assessments.
Recommended Citation
Diraimo, Marc, "Enhancing Brain-Computer Interface (Bci)-Based Color Vision Assessment Subtitle : A New Protocol For Using Steady-State Visual Evoked Potentials To Identify Metamers" (2023). Legacy Theses & Dissertations (2009 - 2024). 3116.
https://scholarsarchive.library.albany.edu/legacy-etd/3116