Common EEG features for behavioral estimation in disparate, real-world tasks.

In this study we explored the potential for capturing the behavioral dynamics observed in real-world tasks from concurrent measures of EEG. In doing so, we sought to develop models of behavior that would enable the identification of common cross-participant and cross-task EEG features. To accomplish this we had participants perform both simulated driving and guard duty tasks while we recorded their EEG. For each participant we developed models to estimate their behavioral performance during both tasks. Sequential forward floating selection was used to identify the montage of independent components for each model. Linear regression was then used on the combined power spectra from these independent components to generate a continuous estimate of behavior. Our results show that oscillatory processes, evidenced in EEG, can be used to successfully capture slow fluctuations in behavior in complex, multi-faceted tasks. The average correlation coefficients between the actual and estimated behavior was 0.548 ± 0.117 and 0.701 ± 0.154 for the driving and guard duty tasks respectively. Interestingly, through a simple clustering approach we were able to identify a number of common components, both neural and eye-movement related, across participants and tasks. We used these component clusters to quantify the relative influence of common versus participant-specific features in the models of behavior. These findings illustrate the potential for estimating complex behavioral dynamics from concurrent measures from EEG using a finite library of universal features.