Emotion Recognition from EEG Signals by Using Empirical Mode Decomposition

This study investigates improved properties of empirical mode decomposition (EMD) for emotion recognition by using electroencephalogram (EEG) signals. The emotion recognition from EEG signals is a difficult study by the reason of nonstationary behavior of the signals. These signals are affected from complicated neural activity of brain. To analyze EEG signals, advanced signal processing techniques are required. In our study, data are collected from one channeled BIOPAC lab system. EEG signals were obtained from visual evoked potentials of 13 female and 13 male volunteers for 12 pleasant and 12 unpleasant pictures. To analyze nonlinear and nonstationary characteristics of EEG signals, an EMD-based method is proposed for emotion recognition. Various time and frequency domain techniques such as power spectral density (PSD), and higher order statistics (HOS) are used to analyze the IMFs extracted by EMD. Support vector machine (SVM), Linear discriminant analysis (LDA), and Naive Bayes classifiers are utilized for the classification of features extracted from the IMFs, and their performances are compared.