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bioRxiv · 10.1101/686907

Quantitative Personality Predictions from a Brief EEG Recording

Li, W.·Wu, C.·Hu, X.·Chen, J.·Fu, S.·Wang, F.·Zhang, D.
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Abstract

The assessment of personality is crucial not only for scientific inquiries but also for real-world applications such as personnel selection. However, most existing ways to quantify personality traits rely on self-reported scales, which are susceptible to biases such as self-presentational concerns. In this study, we propose and evaluate a novel implicit measure of personality that uses machine learning (ML) algorithms to predict an individuals levels in the Big Five personality traits from 5 minutes of electroencephalography (EEG) recordings. Results from a large test sample of 196 volunteers indicated that the personality scores derived from the proposed measure converged significantly with a commonly used questionnaire, predicted behavioral indices and psychological adjustment in a manner similar to self-reported scores, and were relatively stable across time. These evaluations suggest that the proposed measure can serve as a viable alternative to conventional personality questionnaires in practice.

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Li, W., Wu, C., Hu, X., Chen, J., Fu, S., Wang, F., Zhang, D.. 2019-06-28. Quantitative Personality Predictions from a Brief EEG Recording. https://doi.org/10.1101/686907

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