Measuring Extraversion Using EEG Data

Hermann Baumgartl (), Samuel Bayerlein and Ricardo Buettner
Additional contact information
Hermann Baumgartl: Aalen University
Samuel Bayerlein: Aalen University
Ricardo Buettner: Aalen University

A chapter in Information Systems and Neuroscience, 2020, pp 259-265 from Springer

Abstract: Abstract Using a modern fine-graded machine learning approach we show that it is possible to distinguish extraverts from introverts on the basis of resting-state EEG data. We correctly identify extraverts with a prediction performance of 67% and achieve a balanced accuracy of 60.6%. Our results have theoretical and practical implications.

Keywords: Electroencephalography; Machine learning; Big-five; Personality traits; Extraversion (search for similar items in EconPapers)
Date: 2020
References: Add references at CitEc
Citations: View citations in EconPapers (1)

There are no downloads for this item, see the EconPapers FAQ for hints about obtaining it.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:lnichp:978-3-030-60073-0_30

Ordering information: This item can be ordered from
http://www.springer.com/9783030600730

DOI: 10.1007/978-3-030-60073-0_30

Access Statistics for this chapter

More chapters in Lecture Notes in Information Systems and Organization from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().