Brain signal based human emotion analysis by circular back propagation and Deep Kohonen Neural Networks. (May 2018)

• Record Type: Journal Article
• Authors: Hemanth, D. Jude; Anitha, J.; Son, Le Hoang
• Is Part Of: Computers & electrical engineering. Volume 68(2018)
• Page Start: 170
• Page End: 180
• Publication Date: 2018-05
• Subjects: Human emotions -- Artificial neural networks -- Brain signals -- Circular Back Propagation Neural Network (CBPN) -- Deep learning and classification -- Deep Kohonen Neural Network (DKNN); Computer engineering -- Periodicals; Electrical engineering -- Periodicals; Electrical engineering -- Data processing -- Periodicals; Ordinateurs -- Conception et construction -- Périodiques; Électrotechnique -- Périodiques; Électrotechnique -- Informatique -- Périodiques; Computer engineering; Electrical engineering; Electrical engineering -- Data processing; Periodicals; Electronic journals; 621.302854
• DOI: 10.1016/j.compeleceng.2018.04.006
• Languages: English
• ISSNs: 0045-7906

Abstract

Abstract: Human emotion analysis is one of the challenging tasks in today's scenario. The success rate of human emotion recognition has high implication in practical applications such as Human Machine Interaction, anomaly detection, surveillance, etc. Artificial Neural Networks (ANN) is one of the highly favored computational intelligence techniques for human emotion recognition. However, the performance of traditional ANN is not satisfactory in case of applications such as human emotion analysis. This leads to the necessity of modified ANN with better performance than the conventional systems. In this paper, we propose Circular Back Propagation Neural Network (CBPN) and Deep Kohonen Neural Network (DKNN) to overcome drawbacks of the traditional neural networks regarding computational complexity and accuracy. Performance of the proposals is explored in classifying different emotions of humans using Electroencephalography (EEG) signals. It has been validated that the proposals have better performance than the related methods.