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During the last few years, there has been growing interest in the effectsinduced by individual variability on activation patterns and brainconnectivity. The practical implications of individual variability is of basicrelevance for both group level and subject level studies. TheElectroencephalogram (EEG), still represents one of the most used recordingtechniques 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 simpleand easily interpretable features extracted from the EEG power spectra. Inparticular, in an identification scenario, we investigated how the aperiodic(1/f background) component of the EEG power spectra can accurately identifysubjects from a large EEG dataset. The results of this study show that theaperiodic component of the EEG signal is characterized by strongsubject-specific properties, that this feature is consistent across differentexperimental conditions (eyes-open and eyes-closed) and outperforms thecanonically-defined frequency bands. These findings suggest that the simplefeatures (slope and offset) extracted from the aperiodic component of the EEGsignal are sensitive to individual traits and may help to characterize and makeinferences at single subject level.

EEG fingerprinting: subject specific signature based on the aperiodic component of power spectrum