Intelligent human emotion recognition based on elephant herding optimization tuned support vector regression

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.