Brainwaves and Emotions: Machine Learning-Based Analysis of EEG for Affective State Recognition

The present study explores the use of ensemble learning techniques in a machine learning system designed for emotion recognition based on EEG signals. Emotions are critical in decision making, behaviour, and psychological processes. EEG, which records the electrical activity of the brain, offers a non-invasive way of detecting emotion. However, in traditional classification methods, problems arise with generalization when transferring from one individual to another or from varying conditions. For this purpose, the study attempts to combine algorithms of Decision Trees, K-Nearest Neighbours and Naive Bayes into one ensemble framework. The model is evaluated using standard classification metrics.