Electroencephalogram-based emotion recognition: a comparative analysis of supervised machine learning algorithms

Emotion recognition from electroencephalogram (EEG) signals has garnered significant attention owing to its potential applications in affective computing, human-computer interaction, and mental health monitoring. This paper presents a comparative analysis of different machine learning methods for emotion recognition using EEG data. The objective of this study was to identify the most effective algorithm for accurately classifying emotional states using EEG signals. The EEG brainwave dataset: Feeling emotions dataset was used to evaluate the performance of various machine-learning techniques. Multiple machine learning techniques, namely logistic regression (LR), support vector machine (SVM), Gaussian Naive Bayes (GNB), and decision tree (DT), and ensemble models, namely random forest (RF), AdaBoost, LightGBM, XGBoost, and CatBoost, were trained and evaluated. Five-fold cross-validation and dimension reduction techniques, such as principal component analysis, t-distributed stochastic neighbor embedding, and linear discriminant analysis, were performed for all models. The least-performing model, GNB, showed substantially increased performance after dimension reduction. Performance metrics such as accuracy, precision, recall, F1-score, and receiver operating characteristic curves are employed to assess the effectiveness of each approach. This study focuses on the implications of using various machine learning algorithms for EEG-based emotion recognition. This pursuit can improve our understanding of emotions and their underlying neural mechanisms.