Classifying High and Low Self-Esteem using a Novel Machine Learning Method based on EEG Data

Authors

Ricardo Buettner, Aalen UniversityFollow
Daniel Sauter, Aalen UniversityFollow
Isabelle Eckert, Aalen UniversityFollow
Hermann Baumgartl, Aalen UniversityFollow

Paper Type

FP

Paper Number

244

Abstract

While self-esteem is an important concept for health, psychological well-being, and work success, correctly measuring self-esteem is a long-standing and unresolved problem. Here we propose a more objective measure of self-esteem based on electroencephalographic data. Using a novel machine learning approach analyzing specific fine-graded electroencephalographic sub-bands, we can correctly classify high and low self-esteem with an accuracy of over 79 percent, which represents a methodological landmark for health and information systems research. Our results have theoretical, methodological and practical implications.

Recommended Citation

Buettner, Ricardo; Sauter, Daniel; Eckert, Isabelle; and Baumgartl, Hermann, "Classifying High and Low Self-Esteem using a Novel Machine Learning Method based on EEG Data" (2021). PACIS 2021 Proceedings. 39.
https://aisel.aisnet.org/pacis2021/39