title,doi,url,abstract,journal,publication_year,pmid,arxiv
Enhanced EEG-Based Emotion Detection Technique using Deep Belief Network and Wavelet Transform, https://doi.org/10.32628/IJSRCSEIT, https://ijsrcseit.com/CSEIT174408, Today's the role of emotion in communication  brain-computer interface brain diseases and mental states car driver monitoring and recommendation systems is proven. Therefore automatic emotions detection has become one of the most challenging issue. Until now numerous studies have been addressed different technique on improving automatic emotion detection.In this study to achieve bether  validation in classification of emotion by EEG signals we combined wavelet transform with deep belief network. For non-stationary and time-varying are the most important properties of EEG signals we decided to use discrete wavelet transform (sym8) for extracting features such as power then applied deep belief network as a classifier to classify emotions according to two-dimensional arousal-valence model. To examine the effectiveness of  the method we used DEAP database and mapped different emotions on two different classes of valence and arousal. Final results show an acceptable enhancement with the accuracy of 75.52% and 81.03% for valence and arousal respectively., International Journal of Scientific Research in Computer Science Engineering and Information Technology, 2017, CSEIT174408