Emotion Classification Based On Eeg Signal Using Support Vector Machine And Independent Component Analysis

Abstract

Abstract—In making a decision, choose the results of the decision. For example when happy, the show will be fine, on the contrary if sad it will provide bad convenience. Emotions include physiological, namely electroencephalographic (EEG) signals from the brain. EEG recording Appears when electrical problems occur in the brain[1]. EEG signals come from DEAP research: The database for Emotion Analysis uses physiological signals from arousal and valence levels and is processed by Independent Component Analysis (ICA). With ICA, existing data will be processed and obtained new data in the form of a matrix. The results of the matrix will be conveyed by Support Vector Machine (SVM) to produce comfortable conditions when happy, relaxed, nervous, and sad. Thus, the results obtained by the data know what percentage of the index is useful when happy, relax, nervous, and sad.

Index Terms--EEG, ICA, SVM