Emotion Recognition with Multi-Channel EEG Signals Using Auditory Stimulus

Emotions play a significant role in daily life by encouraging the individual in the survival, decision making, guessing, and communication processes. Through emotions can be explained with the activation of anatomical structures in certain regions of brain with nervous system the emotions can be understood by electroencephalogram (EEG) signals. In order to recognize emotions, the signal processing techniques were applied to recorded signals using 32-channels EEG device from the subjects during listening audios. The Self-Assessment Manikin (SAM) form was filled by 23 subjects to evaluate their feelings based on three emotion states and recorded their answers by designed Graphical User Interface (GUI) monitored in front of the subjects. In signal processing stage, the EEG signals were segmented into segmented files by cutting stimulus intervals from recorded signal and decomposed to Intrinsic Mode Functions (IMFs) by Empirical Mode Decomposition (EMD) method. Then, most meaningful IMFs has been selected by analyzing Power Spectral Density (PSD) to extract statistical and entropy-based features and then, classification algorithm has been applied to obtain feature vector to categorize states of emotion consisting of valence, arousal, dominance dimensions. It is aimed to find most useful selected IMFs, most active channels related to emotion, best suitable features for each dimension of emotion. Finally, the percentage of performance accuracy has been calculated and the best accuracy of 81.74% is found in channels ranged in frontal lobe (1–12) for valence state, 72.15% in channel TP7 for arousal state, 74.57% in channels ranged in frontal lobe (1–12) for dominance state by combining different features.