EEG-based affect classification with machine learning algorithms

Abstract

In this paper, we aim to study the EEG-based emotion recognition problem. First, we use clustering algorithm to determine the target class of emotions and perform binary classification of emotion along its arousal and valence dimension. Then we compare two different feature extraction methods, i.e., wavelet transform (resulting in wavelet-based features) and nonlinear dynamics analysis (leading to features of approximate entropy and sample entropy). Five feature reduction algorithms are compared in terms of emotion classification accuracy. Furthermore, four types of machine learning classifiers, including k-nearest neighbor (KNN), naive bayes (NB), support vector machine (SVM) and random forest (RF), are also compared. The results on the DEAP physiological data show that the combination of kernel spectral regression (KSR) and random forest leads to the best binary classification of emotions and that the EEG gamma rhythm is closely correlated to variations in emotions.