Classification of Human Emotions from Electroencephalogram (EEG) Signal using Deep Neural Network
Journal Title: International Journal of Advanced Computer Science & Applications - Year 2017, Vol 8, Issue 9
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.
Authors and Affiliations
Abeer Al-Nafjan, Manar Hosny, Areej Al-Wabil, Yousef Al-Ohali
Keywords
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- EP ID EP261398
- DOI 10.14569/IJACSA.2017.080955
- Views 96
- Downloads 0
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