Emotion Recognition based on PPG and GSR Signals using DEAP Dataset

Emotion recognition is a vital field with applications in various domains. This paper focuses on developing an emotion recognition system using the DEAP dataset, specifically utilizing Photoplethysmography (PPG) and Galvanic Skin Response (GSR) signals. PPG measures changes in blood volume, while GSR records variations in skin conductance, both of which are associated with emotional states. The paper involves data preprocessing, feature extraction, machine learning, and model training. The DEAP dataset provides multi-modal data, including physiological signals, facial expressions, and subjective ratings. By analyzing PPG and GSR signals, distinctive patterns and characteristics related to different emotional states are identified. Machine learning algorithms are employed to build a classification model. The system's performance is evaluated using metrics like accuracy and precision. Successful implementation can contribute to mental health monitoring and improve human-computer interaction by understanding and responding to emotions effectively. This paper advances the field of emotion recognition and enables the development of innovative applications based on physiological signals.