Abstract: Estimation of human emotions from Electroencephalogram (EEG) signals plays a vital role in developing robust Brain-Computer Interface (BCI) systems. In our research, we used Deep Neural Network (DNN) to address EEG-based emotion recognition. This was motivated by the recent advances in accuracy and efficiency from applying deep learning techniques in pattern recognition and classification applications. We adapted DNN to identify human emotions of a given EEG signal (DEAP dataset) from power spectral density (PSD) and frontal asymmetry features. The proposed approach is compared to state-of-the-art emotion detection systems on the same dataset. Results show how EEG based emotion recognition can greatly benefit from using DNNs, especially when a large amount of training data is available.

Abeer Al-Nafjan, Manar Hosny, Areej Al-Wabil and Yousef Al-Ohali, “Classification of Human Emotions from Electroencephalogram (EEG) Signal using Deep Neural Network” International Journal of Advanced Computer Science and Applications(IJACSA), 8(9), 2017. http://dx.doi.org/10.14569/IJACSA.2017.080955

@article{Al-Nafjan2017,
title = {Classification of Human Emotions from Electroencephalogram (EEG) Signal using Deep Neural Network},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2017.080955},
url = {http://dx.doi.org/10.14569/IJACSA.2017.080955},
year = {2017},
publisher = {The Science and Information Organization},
volume = {8},
number = {9},
author = {Abeer Al-Nafjan and Manar Hosny and Areej Al-Wabil and Yousef Al-Ohali}
}