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In the present study we asked whether it is possible to decode personalitytraits from resting state EEG data. EEG was recorded from a large sample ofsubjects (N = 309) who had answered questionnaires measuring personality traitscores of the 5 dimensions as well as the 10 subordinate aspects of the BigFive. Machine learning algorithms were used to build a classifier to predicteach personality trait from power spectra of the resting state EEG data. Theresults indicate that the five dimensions as well as their subordinate aspectscould not be predicted from the resting state EEG data. Finally, to demonstratethat this result is not due to systematic algorithmic or implementationmistakes the same methods were used to successfully classify whether thesubject had eyes open or eyes closed and whether the subject was male orfemale. These results indicate that the extraction of personality traits fromthe power spectra of resting state EEG is extremely noisy, if possible at all.

Personality cannot be predicted from the power of resting state EEG