Analysis of emotional states in parkinson's disease using entropy, Energy-Entropy and teager Energy-Entropy features Rejith K. N1, Subramaniam Kamalraj2 1Research Scholar, Karpagam Academy of Higher Education, Coimbatore 2Associate Professor, Faculty of Engineering, Karpagam Academy of Higher Education, Coimbatore, India Online published on 1 November, 2018. Abstract In this paper, Emotional recognition in Parkinson's disease (PD) has been analyzed in both time and frequency domain using Entropy, Energy-Entropy and Teager Energy-Entropy features. Classification results were observed using three classifiers namely Probabilistic Neural Network, K-Nearest Neighbors Algorithm and Support Vector Machine. Emotional EEG stimuli such as happiness, sadness, fear, anger, surprise, and disgust were used to categorize the PD patients and healthy controls (HC). For each EEG signal, the alpha, beta and gamma band frequency features are obtained for six different feature extraction methods (Entropy, Energy-Entropy, Teager Energy-Entropy, Spectral Entropy, Spectral Energy-Entropy and Spectral Teager Energy-Entropy). The proposed Energy-Entropy feature performs evenly for all six emotions with accuracy of above 80% when compared to other features, whereas different features with classifier gives variant results for few emotions with highest accuracy of above 95%. Top Keywords Electroencephalogram, Emotions, Multimodal stimulus, Non-linear methods, Parkinson's disease, Spreadfactor, Teager Energy Entropy. Top |