Analysis of Multiple Emotions from Electroencephalogram Signals Using Machine Learning Models

Emotion recognition is a valuable technique to monitor the emotional well-being of human beings. It is found that around 60% of people suffer from different psychological conditions like depression, anxiety, and other mental issues. Mental health studies explore how different emotional expressions are linked to specific psychological conditions. Recognizing these patterns and identifying their emotions is complex in human beings since it varies from each individual. Emotion represents the state of mind in response to a particular situation. These emotions, that are collected using EEG electrodes, need detailed emotional analysis to contribute to clinical analysis and personalized health monitoring. Most of the research works are based on valence and arousal (VA) resulting in two, three, and four emotional classes based on their combinations. The main objective of this paper is to include dominance along with valence and arousal (VAD) resulting in the classification of 16 classes of emotional states and thereby improving the number of emotions to be identified. This paper also considers a 2-class emotion, 4-class emotion, and 16-class emotion classification problem, applies different models, and discusses the evaluation methodology in order to select the best one. Among the six machine learning models, KNN proved to be the best model with the classification accuracy of 95.8% for 2-class, 91.78% for 4-class and 89.26% for 16-class. Performance metrics like Precision, ROC, Recall, F1-Score, and Accuracy are evaluated. Additionally, statistical analysis has been performed using Friedman Chi-square test to validate the results.