The field of affective computing aims to enhance the interaction between the human and the machines by identifying emotions and designing applications that automatically adapt to these changes.

Although all the methods provide good classification accuracies, the highest F scores have been obtained using the Random Forest Classifier-89.96% and 85.33% for the two-level and four-level fear evaluation modality.Д®motion is defined as a conscious mental reaction subjectively experienced and directed towards a specific object, accompanied by physiological and behavioral changes in the body. By dividing the ratings of valence/arousal/dominance emotion dimensions, we propose two paradigms for fear level estimation-the two-level (0- no fear and 1- fear) and the four-level (0- no fear, 1- low fear, 2- medium fear, 3- high fear) paradigms. Fear was considered an emotion eliciting low valence, high arousal and low dominance. Having, as a purpose, the development of a system for treating phobias that would automatically determine fear levels and adapt exposure intensity based on the userвЂ™s current affective state, we propose a comparative study between various machine and deep learning techniques (four deep neural network models, a stochastic configuration network, Support Vector Machine, Linear Discriminant Analysis, Random Forest and k-Nearest Neighbors), with and without feature selection, for recognizing and classifying fear levels based on the electroencephalogram (EEG) and peripheral data from the DEAP (Database for Emotion Analysis using Physiological signals) database.

There has been steady progress in the field of affective computing over the last two decades that has integrated artificial intelligence techniques in the construction of computational models of emotion.

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