A DeepLSTM Model for Personality Traits Classification Using EEG Signals

A Deep Long Short-Term Memory (DeepLSTM) Deep Learning model for the classification of personality traits using Electroencephalogram (EEG) signals is developed in this study. The objective is to assess the efficiency of the DeepLSTM model in classification. The publicly available ASCERTAIN EEG dataset is used, which uses the Big Five Factor model for predicting personality. We also evaluated DeepLSTM model performance to that of existing state-of-the-art based machine learning classifiers such as Multilayer Perceptron (MLP), K-Nearest Neighbors (KNN), Support Vector Machine (SVM), and LibSVM. The proposed model outperforms the existing classifiers for the 70-30 partitioning approach, with a maximum classification accuracy of 90.32%.

Automated summary

In this study, a Deep Long Short-Term Memory (DeepLSTM) Deep Learning model for the classification of personality traits using Electroencephalogram (EEG) signals was developed and found to outperform existing machine learning classifiers in terms of classification accuracy.