Independent modulators mediate spectra of multiple brain processes in a VR-based driving experiment

摘要

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