Emotion recognition through EEG phase space dynamics and Dempster-Shafer theory.

Emotions play an important role in our life. Emotion recognition which is considered a subset of brain computer interface (BCI), has drawn a great deal of attention during recent years. Researchers from different fields have tried to classify emotions through physiological signals. Nonlinear analysis has been reported to be successful and effective in emotion classification due to the nonlinear and non-stationary behavior of biological signals. In this study, phase space reconstruction and Poincare planes are employed to describe the dynamics of electroencephalogram (EEG) in emotional states. EEG signals are taken from a reliable database and phase space is reconstructed. A new transformation is introduced in order to quantify the phase space. Dynamic characteristics of the new space are considered as features. Most significant features are selected and samples are classified into four groups including high arousal - high valence (HAHV), low arousal - high valence (LAHV), high arousal - low valence (HALV) and low arousal - low valence (LALV). Classification accuracy was about 90% on average. Results suggest that the proposed method is successful and classification performance is good in comparison with most studies in this field. Brain activity is also reported with respect to investigating brain function during emotion elicitation. We managed to introduce a new way to analyze EEG phase space. The proposed method is applied in a real world and challenging application (i.e. emotion classification). Not only does the proposed method describe EEG changes due to different emotional states but also it is able to represent new characteristics of complex systems. The suggested approach paves the way for researchers to analyze and understand more about chaotic signals and systems.