Publication Date


Document Type


Committee Members

Jennie Gallimore (Advisor), Andy Mckinley (Committee Member), David Reynolds (Committee Member)

Degree Name

Master of Science in Engineering (MSEgr)


A brain stimulation technology called transcranial direct current stimulation (tDCS) may potentially mitigate the vigilance decrement. To practically use such technology, however, a model is necessary that indicates vigilance performance, both when stimulation is being applied and not applied. To address this issue, the author developed models capable of predicting vigilance performance in real and control stimulation conditions using previous tDCS-study data. The "all possible combinations" regression method produced over 200 models, later screened to 10. The model with the best average %error (11.49 ± 0.10) used left hemispheric cerebral blood flow velocity (CBFVL) as its sole input term-accounting for 95.7% of the performance variability (linear best-fit slope of 0.8585). When applied to the control stimulation condition, the model had an average %error of 16.76 ± 0.17 and linear best-fit slope of 0.9278. Such results suggest that CBFVL may be useful as a vigilance performance metric during tDCS applications.

Page Count


Department or Program

Department of Biomedical, Industrial & Human Factors Engineering

Year Degree Awarded