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EEG-Based Personality Prediction Using Fast Fourier Transform and DeepLSTM Model.

Harshit Bhardwaj1, Pradeep Tomar1, Aditi Sakalle1

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

Computational Intelligence and Neuroscience
|October 4, 2021
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This study introduces a Deep Long Short-Term Memory (DeepLSTM) network for classifying personality traits from electroencephalogram (EEG) signals, achieving 96.94% accuracy. The DeepLSTM model outperformed other machine learning methods in personality prediction.

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Area of Science:

  • Neuroscience
  • Artificial Intelligence
  • Psychology

Background:

  • The Myers-Briggs Type Indicator (MBTI) is a widely used model for personality assessment.
  • Electroencephalogram (EEG) signals offer a potential avenue for objective personality trait measurement.
  • Existing machine learning models have limitations in accurately classifying personality from complex biological signals.

Purpose of the Study:

  • To implement and evaluate a Deep Long Short-Term Memory (DeepLSTM) network for personality trait classification using EEG signals.
  • To compare the performance of the proposed DeepLSTM model against established machine learning classifiers.
  • To investigate the efficacy of using emotionally evocative video clips to elicit relevant EEG data for personality analysis.

Main Methods:

  • Collected EEG data from 50 participants using a NeuroSky MindWave Mobile 2 unit while they watched emotionally charged video clips.
  • Utilized the Myers-Briggs Type Indicator (MBTI) framework for defining personality traits.
  • Trained and evaluated a DeepLSTM network, comparing its performance against Artificial Neural Network (ANN), K-nearest neighbors (KNN), LibSVM, and Hybrid Genetic Programming (HGP) using a 10-fold cross-validation method.
  • Validated the DeepLSTM model on the publicly available ASCERTAIN EEG dataset.

Main Results:

  • The DeepLSTM model achieved a maximum classification accuracy of 96.94% for personality trait prediction.
  • The proposed DeepLSTM model demonstrated superior performance compared to ANN, KNN, LibSVM, and HGP.
  • The model showed improved results on the ASCERTAIN EEG dataset, outperforming existing state-of-the-art methods.

Conclusions:

  • DeepLSTM networks are highly effective for classifying personality traits from EEG signals.
  • The developed method offers a promising, objective approach to personality assessment.
  • This research advances the application of deep learning in understanding the neurophysiological correlates of personality.