EEG-Based Emotion Classification for Verifying the Korean Emotional Movie Clips with Support Vector Machine (SVM) - Son, Guiyoung; Kim, Yaeri (Complexity (New York, N.Y.), 2021)
Emotion plays a crucial role in understanding each other under natural communication in daily life. Electroencephalogram (EEG), based on emotion classification, has been widely utilized in the fields of interdisciplinary studies because of emotion representation’s objectiveness. In this paper, it aimed to introduce the Korean continuous emotional database and investigate brain activity during emotional processing. Moreover, we selected emotion-related channels for verifying the generated database using the Support Vector Machine (SVM). First, we recorded EEG signals, collected from 28 subjects, to investigate the brain activity across brain areas while watching movie clips by five emotions (anger, excitement, fear, sadness, and happiness) and a neutral state. We analyzed EEG raw signals to investigate the emotion-related brain area and select suitable emotion-related channels using spectral power across frequency bands, i.e., alpha and beta bands. As a result, we select the eight-channel set, namely, AF3-AF4, F3-F4, F7-F8, and P7-P8, from statistical and brain topography analysis. We perform the classification using SVM and achieve the best accuracy of 94.27% when utilizing the selected channels set with five emotions. In conclusion, we provide a fundamental emotional database reflecting Korean feelings and the evidence of different emotions for application to broaden area.