Three-Way Emotion Classification of EEG-based Signals using Machine Learning

TL;DR

This study evaluates machine learning models for classifying EEG signals into three emotional states, demonstrating that the random forest model outperforms others and existing methods in accuracy and effectiveness.

Contribution

It provides a complete workflow for EEG-based emotion classification and compares multiple ML models, highlighting the superior performance of the random forest approach.

Findings

Random forest achieved the highest accuracy and F1-score.

ML models effectively classify three emotional states from EEG signals.

RF outperforms existing state-of-the-art models in this task.

Abstract

Electroencephalography (EEG) is a widely used technique for measuring brain activity. EEG-based signals can reveal a persons emotional state, as they directly reflect activity in different brain regions. Emotion-aware systems and EEG-based emotion recognition are a growing research area. This paper presents how machine learning (ML) models categorize a limited dataset of EEG signals into three different classes, namely Negative, Neutral, or Positive. It also presents the complete workflow, including data preprocessing and comparison of ML models. To understand which ML classification model works best for this kind of problem, we train and test the following three commonly used models: logistic regression (LR), support vector machine (SVM), and random forest (RF). The performance of each is evaluated with respect to accuracy and F1-score. The results indicate that ML models can be effectively utilized for three-way emotion classification of EEG signals. Among the three ML models trained on the available dataset, the RF model gave the best results. Its higher accuracy and F1-score suggest that it is able to capture the emotional patterns more accurately and effectively than the other two models. The RF model also outperformed the existing state-of-the-art classification models in terms of the accuracy parameter.