Emotion classification using EEG headset signals and Random Forest

TL;DR

This paper presents a machine learning model using EEG signals and Random Forest to classify emotions like happiness, sadness, and relaxation with high accuracy, enabling real-time emotion detection and visualization.

Contribution

It introduces a novel EEG-based emotion classification model utilizing Random Forest and demonstrates its effectiveness with real-time prediction capabilities.

Findings

97.21% accuracy for happiness

76% accuracy for relaxation

76% accuracy for sadness

Abstract

Emotions are one of the important components of the human being, thus they are a valuable part of daily activities such as interaction with people, decision making and learning. For this reason, it is important to detect, recognize and understand emotions using computational systems to improve communication between people and machines, which would facilitate the ability of computers to understand the communication between humans. This study proposes the creation of a model that allows the classification of people's emotions based on their EEG signals, for which the brain-computer interface EMOTIV EPOC was used. This allowed the collection of electroencephalographic information from 50 people, all of whom were shown audiovisual resources that helped to provoke the desired mood. The information obtained was stored in a database for the generation of the model and the corresponding classification analysis. Random Forest model was created for emotion prediction (happiness, sadness and relaxation), based on the signals of any person. The results obtained were 97.21% accurate for happiness, 76% for relaxation and 76% for sadness. Finally, the model was used to generate a real-time emotion prediction algorithm; it captures the person's EEG signals, executes the generated algorithm and displays the result on the screen with the help of images representative of each emotion.