Disentangling periodic and aperiodic resting EEG correlates of personality

Previous studies of the resting electroencephalography (EEG) correlates of personality traits have conflated periodic and aperiodic sources of EEG signals. As each type of activity is associated with different underlying neural dynamics, disentangling these can avoid measurement confounds and clarify interpretation of key findings. Using a large sample (N=300), we investigated how disentangling these activities impacts findings related to two research programs within personality neuroscience. In Study 1 we tested for associations between Extraversion and two putative markers of reward sensitivity—Left Frontal Alpha asymmetry (LFA) and Frontal-Posterior Theta (FPT). In Study 2 we used machine learning to predict personality trait scores from resting EEG. In both studies, power within each EEG frequency bin was quantified as both total power and separate contributions of periodic and aperiodic activity. In Study 1, total power LFA and FPT correlated negatively with Extraversion (r ~ -.14), but this effect disappeared when LFA and FPT were derived only from periodic activity. In Study 2, all Big Five traits could be decoded from periodic power (r ~ .20), and agreeableness could also be decoded from total power and from aperiodic indices. Taken together, these results show how separation of periodic and aperiodic activity in resting EEG recordings allow us to re-evaluate findings in personality neuroscience. Disentangling these signals allows for more reliable findings relating to periodic EEG markers of personality and also provides novel aperiodic measures to be explored in this field.