A Novel Approach to the Classification of Human Emotion and Analysis of Complete Mental State Using Brain EEG Signals

Abstract

In the area of brain-computer interface, Intelligent emotion detection based on Electroencephalography brain signals is of great significance. Currently, deep learning algorithms like DNN, CNN, and SVM have significantly improved detection and prediction accuracy in many fields. However, deep learning and SVM have certain limitations in perceiving global dependence. In the present scenario, most of the deep learning models rely on pre-processing, extracting features, and network topology but still are not sufficient to provide satisfactory accuracy for the small and noisy database. Overlapping in target classes and boundaries causes low performance of SVM no matter the dataset is highly dimensional with fewer samples. In this research study, the focus of the novel approach is on developing a classification strategy for working on more emotion types. A “Mean of Mean “algorithm is proposed to completely analyze mental state by considering all the features in the features set. Emotion is first classified into one of the quadrants from four quadrants of emotion by comparing with the referential mean and then depending on the intensity of arousal the emotion is further classified into 12 subtypes by using the MIN Max range. The proposed algorithm performed better compared to other algorithms and provide a wide range of emotion types. When compared to current research of multi-class emotion identification, the experimental findings demonstrated that the suggested technique is extremely competitive. The average accuracy rate was above 90%, and it provided a comprehensive assessment of the mental condition. On the emotional spectrum, a person's emotional state is assessed.