A SYSTEMS ENGINEERING BASED METHODOLOGY FOR ANALYZING HUMAN BRAIN FUNCTION (EVOKED RESPONSES, STEADY-STATE EEG)
Date of Completion
The objective of this work was to develop a systems engineering based methodology for quantifying effects of cognitive loading, and for modeling the human brain response in systems engineering terms. The visual-cortical response was selected as the input-output channel around which the methodology was developed. A steady-state input (a continuous sum of 10 sine waves) and a transient input (a train of pulses) were used for system stimulation. The human occipital EEG (surface electrodes at Oz and mastoids) was used as the cortical output. Analysis of input stimuli and output EEG potentials produced human visual-cortical describing functions (gain and phase) and remnant spectra (background EEG).^ A stimulus parametric investigation indicated that sensitivity to stimulus modulation depth is unequal across frequencies, and that relatively low stimulus intensity and depth of modulation are desirable to reduce response saturation.^ Comparisons between transient and steady-state stimulation revealed that both forms produced functionally related responses, suggesting that the visual-cortical response contains a measurable linear portion. Time domain amplitude changes corresponded to transient and steady-state gain changes.^ Consistent trends observed among subjects indicated that it is advisable to group subjects into two classes: alpha responders, and non-alpha responders. Classification would be based upon alpha band (8 to 12 Hz) responses in both remnant and gain curves.^ To investigate cognitive loading effects three tasks were utilized: manual tracking, grammatical reasoning, and supervisory control. Changes in visual-cortical response measures related to task loading were observed. With increased cognitive loading, alpha responders generally showed reductions in alpha band gain and remnant, and increases in beta band gain. Non-alpha responders showed increases in beta band gain only. Performance of supervisory control caused a reduction in phase lag for all subjects tested. Results revealed that individual differences must be accounted for.^ Some success in matching data with the model forms considered was achieved. Multipath modeling provided a promising approach for handling observed dropout points in the data. Model parameter values were found to relate to task performance scores. A model form based upon alpha and non-alpha classification should be individually applied. ^
JUNKER, ANDREW MICHAEL, "A SYSTEMS ENGINEERING BASED METHODOLOGY FOR ANALYZING HUMAN BRAIN FUNCTION (EVOKED RESPONSES, STEADY-STATE EEG)" (1986). Doctoral Dissertations. AAI8709038.