Date of Award

2013

Document Type

Dissertation

Degree Name

Doctor of Philosophy (PhD)

Department

Systems Engineering

First Advisor

Ntuen, Dr. Celestine A.

Abstract

Two tasks were investigated in this study. The first study investigated the effects of alignment display errors on visual fatigue. The experiment revealed the following conclusive results: First, EEG data suggested the possibility of cognitively-induced time compensation changes due to a corresponding effect in real-time brain activity by the eyes trying to compensate for the alignment. The magnification difference error showed more significant effects on all EEG band waves, which were indications of likely visual fatigue as shown by the prevalence of simulator sickness questionnaire (SSQ) increases across all task levels. Vertical shift errors were observed to be prevalent in theta and beta bands of EEG which probably induced alertness (in theta band) as a result of possible stress. Rotation errors were significant in the gamma band, implying the likelihood of cognitive decline because of theta band influence. Second, the hemodynamic responses revealed that significant differences exist between the left and right dorsolateral prefrontal due to alignment errors. There was also a significant difference between the main effect for power band hemisphere and the ATC task sessions. The analyses revealed that there were significant differences between the dorsal frontal lobes in task processing and interaction effects between the processing lobes and tasks processing. The second study investigated the effects of cognitive response variables on visual fatigue. Third, the physiologic indicator of pupil dilation was 0.95mm that occurred at a mean time of 38.1min, after which the pupil dilation begins to decrease. After the average saccade rest time of 33.71min, saccade speeds leaned toward a decrease as a possible result of fatigue on-set. Fourth, the neural network classifier showed visual response data from eye movement were identified as the best predictor of visual fatigue with a classification accuracy of 90.42%. Experimental data confirmed that 11.43% of the participants actually experienced visual fatigue symptoms after the prolonged task.

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