This study explores the use of Independent Component Analysis (ICA) applied to normalized logarithmic spectral changes in the activities of brain processes separated by spatial filters learned from electroencephalogram (EEG) data using a temporal ICA. EEG data were collected during 1-2 hour virtual-reality based driving experiments, in which subjects were instructed to maintain their cruising position and compensate for randomly induced drifts using the steering wheel. ICA was first applied to 30-channel EEG data to separate the recorded signals into a sum of maximally temporally independent components (ICs) for each of 15 subjects. Logarithmic spectra of IC activities were then submitted to PCA-ICA to find spectrally fixed and temporally independent modulator (IM) processes. The second ICA detected and modeled independent co-modulatory systems that multiplicatively affect the activities of spatially distinct IC processes. Across subjects, we found two consistent temporally independent modulators: theta-beta and alpha modulators that mediate spectral activations of the distinct cortical areas when the participants experience waves of alternating alertness and drowsiness during long hour simulated driving. Furthermore, the time courses of the theta-beta modulator were highly correlated with concurrent changes in subject driving error (a behavioral index of drowsiness).