  During the last few years, there has been growing interest in the effects
induced by individual variability on activation patterns and brain
connectivity. The practical implications of individual variability is of basic
relevance for both group level and subject level studies. The
Electroencephalogram (EEG), still represents one of the most used recording
techniques to investigate a wide range of brain related features. In this work,
we aim to estimate the effect of individual variability on a set of very simple
and easily interpretable features extracted from the EEG power spectra. In
particular, in an identification scenario, we investigated how the aperiodic
(1/f background) component of the EEG power spectra can accurately identify
subjects from a large EEG dataset. The results of this study show that the
aperiodic component of the EEG signal is characterized by strong
subject-specific properties, that this feature is consistent across different
experimental conditions (eyes-open and eyes-closed) and outperforms the
canonically-defined frequency bands. These findings suggest that the simple
features (slope and offset) extracted from the aperiodic component of the EEG
signal are sensitive to individual traits and may help to characterize and make
inferences at single subject level.
