Date of Award
Master of Science
Department of Electrical and Computer Engineering
Brett J. Borghetti, PhD
Trust is a pillar of society and is a fundamental aspect in every relationship. With the use of automated agents in todays workforce exponentially growing, being able to actively monitor an individuals trust level that is working with the automation is becoming increasingly more important. Humans often have miscalibrated trust in automation and therefore are prone to making costly mistakes. Since deciding to trust or distrust has been shown to correlate with specific brain activity, it is thought that there are EEG signals which are associated with this decision. Using both a human-human trust and a human-machine trust EEG dataset from past research, within-participant, cross-participant, and cross-task cross-participant trust detection was attempted. Six machine learning models, logistic regression, LDA, QDA, SVM, RFC, and an ANN, were used for each experiment. Multiple within-participant models had balanced accuracies greater than 70.00 , but no cross-participant or cross-participant cross task models achieved this.
DTIC Accession Number
Elkins, James R., "Comparison of Machine Learning Techniques on Trust Detection Using EEG" (2021). Theses and Dissertations. 4893.