Abstract
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.
Highlights
Emotion is the most important human factor and plays a vital role in daily life
EEG has more various benefits, for example, easy use, a high time resolution, and direct measurement compared to other signals [3, 4]
To improve the emotion classification, we changed the hyperparameter as the following range: (1) Parameter C: 0, 1000 (2) Parameter c: 0.00001, 1, (1/Nx)varx
Summary
Emotion is the most important human factor and plays a vital role in daily life. it provides diverse information about the human experience. E physiological signals provide more objective and appropriate information for representing the emotional states than behavioral responses such as the face and voice [2] Among these physiological signals, EEG has more various benefits, for example, easy use, a high time resolution, and direct measurement compared to other signals [3, 4]. EEG has more various benefits, for example, easy use, a high time resolution, and direct measurement compared to other signals [3, 4] For this reason, many researchers prefer EEG signals to the emotion classification usage to achieve the reliable outputs in responses to emotional states [5]
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