Intelligent human emotion recognition based on elephant herding optimization tuned support vector regression. (August 2018)

• Record Type: Journal Article
• Authors: Hassanien, Aboul Ella; Kilany, Moataz; Houssein, Essam H.; AlQaheri, Hameed
• Is Part Of: Biomedical signal processing and control. Volume 45(2018)
• Page Start: 182
• Page End: 191
• Publication Date: 2018-08
• Subjects: Electroencephalography (EEG) -- Discrete wavelet transform (DWT) -- Elephant herding optimization (EHO) -- Support vector regression (SVR) -- Radial basis function (RBF) -- Feature selection; Signal processing -- Periodicals; Biomedical engineering -- Periodicals; Signal Processing, Computer-Assisted -- Periodicals; Image Processing, Computer-Assisted -- Periodicals; Biomedical Engineering -- Periodicals; 610.28
• DOI: 10.1016/j.bspc.2018.05.039
• Languages: English
• ISSNs: 1746-8094

Abstract

Highlights: Implement an intelligent human emotion recognition based on elephant herding optimization tuned support vector regression. Discrete wavelet transform (DWT) was applied for features extraction electroencephalography signals. Predicted three emotional scales as continuous variables including valence, dominance, and arousal. Results of emotion regression by SVR classifier show that EHO can improve regression accuracy significantly. Abstract: The ability to recognize emotional states of people surrounding us is an important portion of natural communication as emotions are fundamental factors in human decision handling, interaction, and cognitive procedure. The primary intention of this paper is to present an approach that uses electroencephalography (EEG) signals to recognize human emotions. This work targets emotional recognition in terms of three emotional scales; valence, arousal and dominance. EEG raw data were pre-processed to remove artifacts, discrete wavelet transform (DWT) was applied for features extraction. Moreover, support vector regression (SVR) is combined with Elephant herding optimization (EHO) to predict values of the three emotional scales as continuous variables. Multiple experiments are applied to evaluate prediction performance. EHO was applied in two stages of optimization. Firstly, to fine-tune regression parameters of the SVR. Secondly, to select the most relevant features extracted from all 40 EEG channels and eliminate ineffective and redundant features. To verify the proposed approach, results proved EHO-SVR ability to gain relatively enhanced performance measured by regression accuracy of 98.64%. Therefore, SVR is introduced in this paper as a better technique for predicting emotions as quantifiable continuous variables rather than classifying emotions into discrete emotional values.