Paper Info
Title
Brain signal based human emotion analysis by circular back propagation and Deep Kohonen Neural Networks.
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.
Year
DOI
Venue
2018
10.1016/j.compeleceng.2018.04.006
Computers & Electrical Engineering
Keywords
Field
DocType
Human emotions,Artificial neural networks,Brain signals,Circular Back Propagation Neural Network (CBPN),Deep learning and classification,Deep Kohonen Neural Network (DKNN)
Anomaly detection,Computational intelligence,Emotion recognition,Computer science,Real-time computing,Kohonen neural network,Artificial intelligence,Backpropagation,Artificial neural network,Electroencephalography,Computational complexity theory
Journal
Volume
ISSN
Citations 
68
0045-7906
7
PageRank 
References 
Authors
0.41
18
3
Name
Order
Citations
PageRank
D. Jude Hemanth111922.74
J. Anitha210016.43
Le Hoang Son386164.51