A Study of Arousal Classification Based on EEG Signal with Support Vector Machine Method

Abstract

Abstract---The development of Brain Computer Interface technology nowadays has spread out in a case of classifying emotions based on brain signal (EEG) in human, which in this work using a set of secondary data from DEAP. One of the emotion parameters being focused on here is arousal with the range from low (uninterested) to high (excited). This study is applying Principal Component Analysis as the feature extraction. Not only that, feature extraction also being done statistically. As for feature classification is using Support Vector Machine with the maximum accuracy that only able to reach 59,4% which still needs improvements in the system for future works. Keywords---EEG, PCA, SVM, Emotion Classification