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Automatic Recognition of Personality Profiles Using EEG Functional Connectivity During Emotional Processing.

Manousos A Klados1,2, Panagiota Konstantinidi2,3, Rosalia Dacosta-Aguayo4

  • 1Department of Psychology, University of Sheffield, International Faculty, CITY College, Thessaloniki 54453, Greece.

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This study accurately predicts personality traits using electroencephalogram (EEG) data during emotion processing. Machine learning models achieved high accuracy in identifying individual personality profiles from brain activity.

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Big-Five factor modelbrain functional connectivityelectroencephalogram signal processingemotional processingneurosciencepersonality detection

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Area of Science:

  • Neuroscience and Cognitive Science
  • Human-Computer Interaction (HCI)
  • Machine Learning Applications

Background:

  • Personality recognition is key for personalized and realistic human-computer interaction.
  • Understanding the neurobiological basis of personality requires analyzing brain activity.
  • Previous attempts using resting-state electroencephalogram (EEG) showed limited success in personality prediction.

Purpose of the Study:

  • To develop an automated method for personality recognition using EEG signals.
  • To investigate the efficacy of EEG data acquired during emotion processing for personality profiling.
  • To combine neuroscientific data with machine learning for enhanced user experience in HCI.

Main Methods:

  • Utilized the AMIGOS dataset comprising EEG recordings from 37 healthy participants during emotion processing.
  • Extracted brain network and graph theoretical parameters from cleaned EEG signals.
  • Employed k-means for dichotomizing personality trait scores, feature selection for identifying key features, and Support Vector Machines (SVM) for classification.

Main Results:

  • Achieved high classification accuracies for major personality traits: 83.8% for extraversion, 86.5% for agreeableness, 83.8% for conscientiousness, and 83.8% for neuroticism.
  • Demonstrated a classification accuracy of 73% for openness.
  • Successfully identified distinct personality profiles from EEG data with significant accuracy.

Conclusions:

  • EEG recordings during emotion processing are effective for automated personality recognition.
  • Machine learning techniques, particularly SVM, can accurately classify personality traits based on neurophysiological data.
  • This approach holds promise for creating more adaptive and user-friendly HCI applications by understanding user personality.