Classification of Human Emotion Using DT-SVM Algorithm with Enhanced Feature Selection and Extraction

Abstract

Emotions are a basic component of human life. It generates different brain waves for emotions such as happiness, sadness, anger, calmness, tension, excitement, etc. The brain waves are electric and their electric impulse can be measured and recorded as a continuous stream of data. These emitted brain waves are recorded using an EEG device. Many existing systems are in use that feeds the recorded data into various Machine learning algorithms to classify the emotions. These systems are huge and complex, thus require a great amount of time for initializing and working. While a lot of algorithms are used and new algorithms are discovered to classify Brain EEG data, most of the time results will be improper and will not be reliable. The proposed system extracts only the data which corresponds to Human-emotions from the continuous stream of EEG data. The system makes use of robust preprocessing algorithms like ANOVA and PCA for feature extraction and selection to identify and extract features associated with Human-emotion. Later, these recording signals are modeled and fed into Dynamic Time wrapping Simple vector machine (DT-SVM) classification algorithm to analyze and predict the emotion of the person during the experiment which produces an improved accuracy of 99.2%compared to existing system.