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Personality Prediction with Hybrid Genetic Programming using Portable EEG Device.

Harshit Bhardwaj1, Pradeep Tomar1, Aditi Sakalle1

  • 1Department of CSE, USICT, Gautam Buddha University, Greater Noida, India.

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This study uses electroencephalogram (EEG) to identify personality traits from film clips. Hybrid genetic programming achieved 82.25% accuracy in classifying Myers-Briggs Type Indicator (MBTI) traits from brainwave data.

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Area of Science:

  • Neuroscience
  • Psychology
  • Machine Learning

Background:

  • Personality traits influence emotional responses to stimuli.
  • Electroencephalogram (EEG) captures brain activity related to emotional states.
  • The Myers-Briggs Type Indicator (MBTI) is a widely used personality assessment tool.

Purpose of the Study:

  • To develop a real-time method for identifying personality traits using EEG signals.
  • To correlate specific brainwave patterns with MBTI personality types.
  • To explore the application of machine learning in personality assessment.

Main Methods:

  • Utilized a single-channel NeuroSky MindWave 2 for EEG data acquisition.
  • Employed Fast Fourier Transform (FFT) for feature extraction from EEG signals.
  • Applied Hybrid Genetic Programming (HGP) for classifying EEG data against MBTI traits.

Main Results:

  • Developed four two-class HGP classifiers for different MBTI trait groups.
  • Achieved an overall classification accuracy of 82.25% using 10-fold cross-validation.
  • Demonstrated the feasibility of real-time personality trait identification from EEG.

Conclusions:

  • EEG data, analyzed with FFT and HGP, can effectively predict personality traits.
  • This approach offers a novel, objective method for personality assessment.
  • Future research can refine the model for broader applications in psychology and human-computer interaction.