Emotion recognition using EEG phase space dynamics and Poincare intersections

Emotions play a crucial role in our daily life. Emotion recognition has been used in numerous areas such as education, rehabilitation, etc. Simple to record and cost-effective, Electroencephalogram (EEG)-based emotion classification has been attracting a great deal of attention so far. Since our feelings are controlled by our brain which is inherently complex, it is imperative to employ nonlinear methods especially EEG phase space as it contains valuable information about EEG dynamics. In this study, EEG phase space is reconstructed and transformed into a new state space. Poincare planes are utilized to describe the proposed state space mathematically. They quantify EEG dynamics. Poincare intersections are extracted as features and then fed to the classification models including multi-layer perceptron (MLP), k-nearest neighbor (KNN) and multi-class support vector machine (MSVM). Variable and constant number of Poincare planes are considered and three different approaches are taken to determine optimum planes. A very reliable database is used and different aspects are considered to test the proposed method fairly. We employ three different evaluation scenarios including leave-one-subject-out, leave-one-trial-out and ten-fold cross validation and the recognition rates for all the scenarios are above 70% which is comparable to the previous studies. Not only is the proposed method effective in emotion recognition but it also introduces a novel approach to nonlinear signal processing which can also be employed in other applications and describe signals' complex dynamics appropriately. (C) 2020 Elsevier Ltd. All rights reserved.