Real-time fractal-based valence level recognition from EEG

Abstract

Emotions are important in human-computer interaction. Emotions could be classified based on 3-dimensional Valence-Arousal-Dominance model which allows defining any number of emotions even without discrete emotion labels. In this paper, we propose a real-time fractal dimension (FD) based valence level recognition algorithm from Electroencephalographic (EEG) signals. The FD-based feature is proposed as a valence dimension index in continuous emotion recognition. The thresholds are used to identify different levels of the valence dimension. The algorithm is tested on the EEG data labeled with different valence levels from the proposed and implemented experiment database and from the benchmark affective EEG database DEAP. The proposed algorithm is applied for recognition of 16 emotions defined by high/low arousal, high/low dominance and 4 levels of valence dimension. 9 levels of valence states with controlled dominance levels (high or low) can be recognized as well. The proposed algorithm can be implemented in different real-time applications such as emotional avatar and E-learning systems. © 2013 Springer-Verlag Berlin Heidelberg.