A New feature extraction method to Improve Emotion Detection Using EEG Signals
Zamanian, Hanieh (University of Birjand (Birjand, Iran). Department of Electrical and Computer Engineering)
Farsi, Hassan (University of Birjand (Birjand, Iran). Department of Electrical and Computer Engineering)

Fecha:	2018
Resumen:	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.
Derechos:	Aquest document està subjecte a una llicència d'ús Creative Commons. Es permet la reproducció total o parcial, la distribució, i la comunicació pública de l'obra, sempre que no sigui amb finalitats comercials, i sempre que es reconegui l'autoria de l'obra original. No es permet la creació d'obres derivades.
Lengua:	Anglès
Documento:	Article ; recerca ; Versió publicada
Materia:	Emotion recognition ; EEG ; Arousal-valence emotion model ; Support vector machine ; Neural network
Publicado en:	ELCVIA : Electronic Letters on Computer Vision and Image Analysis, Vol. 17 Núm. 1 (2018) , p. 29-44 (Regular Issue) , ISSN 1577-5097

Adreça original: https://elcvia.cvc.uab.es/article/view/v17-n1-zamanian
Adreça alternativa: https://raco.cat/index.php/ELCVIA/article/view/v17-n1-zamanian
DOI: 10.5565/rev/elcvia.1045

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