Selection of valid and reliable EEG features for predicting auditory and visual alertness levels.

A selection procedure with three rules, high efficiency, low individual variability, and low redundancy, was developed to screen electroencephalogram (EEG) features for predicting behavioral alertness levels. A total of 24 EEG features were derived from temporal, frequency spectral, and statistical analyses. Behavioral alertness levels were quantified by correct rates of performance on an auditory and a visual vigilance task, separately. In the auditory task study, a subset of three EEG features, the relative spectral amplitudes in the alpha (alpha%, 8-13 Hz) and theta (theta%, 4-8 Hz) bands, and the mean frequency of the EEG spectrum (MF), was found to be the best combination for predicting the auditory alertness level. In the visual task study, the mean frequency of the beta band (Fbeta, 13-32 Hz) was the only EEG feature selected. The application of an averaging subwindow procedure within a moving time window to EEG analysis increased the predictive power of EEG features and decreased the disturbing effect of movement artifacts on the EEG data.