Classification of emotional states in parkinson's disease patients using machine learning algorithms

Individuals with Parkinson's disease have been stressed and shown difficulty in various emotion recognition. In recent years, numerous studies have been conducted in emotion recognition of Parkinson's disease (PD). EEG signals helps to find out the connections between emotional condition and its brain activities. In this paper, classification of EEG based emotion recognition in Parkinson's disease was analyzed using four features and two classifiers. Six 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 four different feature extraction methods (Entropy, Energy-Entropy, Spectral Entropy and Spectral Energy-Entropy). The extracted features are then associated to different control signals and two different models (Probabilistic Neural Network and K-Nearest Neighbors Algorithm) have been developed to observe the classification accuracy of these four features. The proposed combination feature, 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%.