Classification of Emotional State Based on EEG Signal using AMGLVQ

Abstract

Identifying humans emotional state using electroencephalogram (EEG) signal more precisely than using non-verbal and verbal signals, because emotions are psychological and physiological processes that are connected with personality, motivation, mood, and temperament. EEG is a physiological signal that recorded from brain activity in the form of brain waves through the scalp. In this study, emotional states will be identified based on EEG signals using the Adaptive Multilayer Generalized Learning Vector Quantization (AMGLVQ) algorithm. The dataset used is DEAP: A Database for Emotion Analysis using Physiological and Audiovisual Signals. Emotional conditions that are classified are valence, that is low and high valence. DEAP dataset has imbalanced data characteristics, and one of the advantages of AMGLVQ algorithm is handling classification in imbalanced data conditions. The test results show that AMGLVQ has better performance compared to Random Forest (RF) and Support Vector Machine (SVM).