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EEG Sensor-Based Computational Model for Personality and Neurocognitive Health Analysis Under Social Stress.

Majid Riaz1, Pedro Guerra2, Raffaele Gravina1

  • 1Department of Informatics, Modeling, Electronics and System Engineering, University of Calabria, 87036 Rende, Italy.

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Summary

This study links personality traits to brain activity using EEG and machine learning. Higher scores in Extraversion, Conscientiousness, and Openness correlate with better stress recovery and cognitive resilience.

Keywords:
biosignal processingcognitive resilienceelectroencephalography (EEG)human-centric AImachine learningmental healthneural oscillationsneurosciencepersonality traitssocial stresstheta–beta ratio (TBR)

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Area of Science:

  • Neuroscience
  • Computational Psychology
  • Biosignal Processing

Background:

  • Personality traits influence cognitive health and brain activity.
  • Understanding the neural basis of personality is crucial for mental health.
  • Existing methods for personality assessment lack objective neural correlates.

Purpose of the Study:

  • To develop an EEG sensor-based computational framework for quantifying personality traits.
  • To investigate the relationship between personality traits and neural dynamics during stress.
  • To decode the Big Five personality traits using machine learning on EEG data.

Main Methods:

  • Electroencephalography (EEG) recordings from 21 participants during the Trier Social Stress Test (TSST).
  • Machine learning (ML) algorithms (SVM, MLP) applied to 64-electrode EEG data to classify Big Five personality traits.
  • Multiphase neurocognitive analysis across TSST stages (baseline, mental arithmetic, job interview, recovery).

Main Results:

  • Significant negative correlations found between frontal-temporal theta-beta ratio (TBR) and Extraversion, Conscientiousness, and Openness.
  • Elevated trait scores indicate faster stress recovery and higher cognitive resilience.
  • High classification accuracy achieved for all Big Five traits (81.5%–94.7%).

Conclusions:

  • Empirical validation of the alignment between personality constructs and neural oscillatory patterns.
  • EEG-based sensing and ML analytics show potential for personalized mental health monitoring.
  • Framework supports human-centric AI systems attuned to individual neurocognitive profiles.