Automated Detection and Mitigation of Inefficient Visual Searching Using Electroencephalography and Machine Learning
Date of Award
Master of Science in Computer Science
Department of Electrical and Computer Engineering
Brett J. Borghetti, PhD
Decisions made during the high-stress and fast-paced operations of the military are extremely prone to cognitive biases. A commonly known cognitive bias is a confirmation bias, or the inappropriate bolstering of an unknown hypothesis. One such critical military operation that can fall prey to a confirmation bias is a visual search. During a visual search, a military operator must perform a visual scan of an environment for a specific target. However, the visual search process can fall prey to the same confirmation bias which can cause inefficient searches. This study elicits inefficient visual search patterns and applies various mitigation techniques in an effort to improve the efficiency of the searches. The effects of the various mitigations are studied and the most effective mitigations are determined. Machine learning models are trained to find the relationship between Electroencephalography (EEG) signals and inefficient visual searching.
DTIC Accession Number
Gallaher, Joshua P., "Automated Detection and Mitigation of Inefficient Visual Searching Using Electroencephalography and Machine Learning" (2020). Theses and Dissertations. 3160.