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Abstract
In the present study we asked whether it is possible to decode personality traits from resting state EEG data. EEG was recorded from a large sample of subjects (n = 289) who had answered questionnaires measuring personality trait scores of the five dimensions as well as the 10 subordinate aspects of the Big Five. Machine learning algorithms were used to build a classifier to predict each personality trait from power spectra of the resting state EEG data. The results indicate that the five dimensions as well as their subordinate aspects could not be predicted from the resting state EEG data. Finally, to demonstrate that this result is not due to systematic algorithmic or implementation mistakes the same methods were used to successfully classify whether the subject had eyes open or closed. These results indicate that the extraction of personality traits from the power spectra of resting state EEG is extremely noisy, if possible at all.
Highlights
Personality can be defined as a relatively stable pattern on thinking, feeling, and acting
Machine learning algorithms were used to build a classifier to predict each personality trait from power spectra of the resting state EEG data.The results indicate that the five dimensions as well as their subordinate aspects could not be predicted from the resting state EEG data
To demonstrate that this result is not due to systematic algorithmic or implementation mistakes the same methods were used to successfully classify whether the subject had eyes open or closed. These results indicate that the extraction of personality traits from the power spectra of resting state EEG is extremely noisy, if possible at all
Summary
Personality can be defined as a relatively stable pattern on thinking, feeling, and acting. According to a dominant five factor model (FFM), observable personality is mostly determined by five major traits – Neuroticism, Extraversion, Openness, Agreeableness, and Conscientiousness (McCrae and John, 1992; McCrae and Costa, 2008) Their relatively high cultural universality, temporal stability, and heritability suggest that the Big Five traits may represent some sufficiently stable parameters of fairly specific brain networks (Corr, 2004; DeYoung and Gray, 2009; Kennis et al, 2012). An early hypothesis relating Extraversion to baseline brain arousal turned out to be a gross oversimplification (Stelmack, 1990) Another influential idea linking anterior asymmetry in EEG alpha (8–12 Hz) band power to individual differences in approach and avoidance systems of the brain (Davidson, 2001; Coan and Allen, 2002), has not been confirmed using meta-analytic methods (Wacker et al, 2010)
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