A New feature extraction method to Improve Emotion Detection Using EEG Signals

• Main Authors: Hanieh Zamanian, Hassan Farsi
• Format: Article
• Language: English
• Published: Computer Vision Center Press 2018-11-01
• Series: ELCVIA Electronic Letters on Computer Vision and Image Analysis
• Subjects: emotion recognition; EEG; Arousal-Valence emotion model; support vector machine; neural network.
• Online Access: https://elcvia.cvc.uab.es/article/view/1045

Since emotion plays an important role in human life, demand and importance of automatic emotion detection have grown with increasing role of human computer interface applications. In this research, the focus is on the emotion detection from the electroencephalogram (EEG) signals. The system derives a mechanism of quantification of basic emotions using. So far, several methods have been reported, which generally use different processing algorithms, evolutionary algorithms, neural networks and classification algorithms. The aim of this paper is to develop a smart method to improve the accuracy of emotion detection by discrete signal processing techniques and applying optimized support vector machine classifier with genetic evolutionary algorithm. The obtained results show that the proposed method provides the accuracy of 93.86% in detection of 4 emotions which is higher than state-of-the-art methods.