Determination of Emotional Status From Eeg Time Series by Using Emd Based Local Binary Pattern Method

Abstract

Although detecting emotional states from brain dynamics is an issue that has been studied for a long time, the desired level has not been reached yet. In this study, Empirical mode decomposition (EMD) based Local Binary Pattern (LBP) method is proposed to determine emotional states using (positive-neutral-negative) Electroencephalogram (EEG) signals. By using this method, a hybrid structure is created to obtain features from EEG signals. In the study, Seed EEG dataset including 15 positive subjects and positive-neutral-negative emotional state is used. In proposed method, a classification task is utilized with the basis of individuals by using 27 EEG channels from left hemisphere of each subject. Level 5 is separated by applying EMD to EEG segments that contain three emotional states. Features are obtained from the Intrinsic Mode Functions (IMFs) using LBP method. These features are classified with k Nearest Neighbor (k-NN) and Artificial Neural Network (ANN). The average classification accuracy for 15 participants is 83.77% with k-NN classifier and 84.50% with the ANN classifier. Furthermore, the highest classification performance is found to be 96.75% with the k-NN classifier. The results obtained in the study support related studies in the literature.