A systematic literature review of machine learning-based personality trait detection using electroencephalographic data

Rieck, Celina; Penava, Pascal; Buettner, Ricardo

doi:10.1109/access.2025.3586005

A systematic literature review of machine learning-based personality trait detection using electroencephalographic data

Publication date

2025-07

Document type

Übersichtsartikel, Überblicksdarstellung

Author

Rieck, Celina

Penava, Pascal

Buettner, Ricardo

Organisational unit

Hybrid Intelligence

DOI

10.1109/access.2025.3586005

URI

https://openhsu.ub.hsu-hh.de/handle/10.24405/23070

Publisher

IEEE

Series or journal

IEEE Access

ISSN

2169-3536

Periodical volume

13

First page

114812

Last page

114833

Peer-reviewed

✅

Part of the university bibliography

✅

Language

English

Abstract

Traditional self-report personality assessments, such as detecting Big Five personality traits through the NEO-Five-Factor-Inventory, are personnel-, time- and cost-intensive approaches that are prone to bias, whereas electroencephalography provides a biometric alternative by capturing neural correlates of personality. That is why machine learning-based detection of personality traits using electroencephalographic data has gained increasing attention. The integration of machine and deep learning algorithms has improved automated trait classification but the comparative reliability of these approaches remains unclear. This systematic literature review examines if trait detection is possible by using electroencephalography in combination with machine and deep learning models, analyzing 58 studies since 2015. We compare their performance by a meta-analysis of weighted data, highlighting strengths and limitations. Our findings indicate that machine and deep learning models achieve varying degrees of accuracy in detecting different personality traits, with Openness demonstrating the highest classification accuracy, while Neuroticism remains the most challenging to detect. Deep learning models, particularly hybrid architectures, outperform traditional machine learning classifiers, highlighting the advantage of deep feature extraction in EEG data processing. However, methodological inconsistencies, limited dataset standardization and the influence of transient emotional states pose significant challenges. Despite these limitations, machine and deep learning models based on electroencephalography show promise for applications in mental health, recruitment and personalized interventions. Further research is needed to standardize methods and improve model reliability for real-world application.

Description

This work is licensed under a Creative Commons Attribution 4.0 License. For more information, see https://creativecommons.org/licenses/by/4.0.

Version

Published version

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