Physiological Signal-Based Real-Time Emotion Recognition Based on Exploiting Mutual Information with Physiologically Common Features

This paper proposes a real-time emotion recognition system that utilizes photoplethysmography (PPG) and electromyography (EMG) physiological signals. The proposed approach employs complex-valued neural network to extract common features from the signals, enabling successful without interference. comprises three stages: single-pulse extraction, coherence feature module, module. experimental results demonstrate method surpasses alternative approaches in terms of accuracy interval. By extracting PPG EMG this achieves effective mutual findings provide significant advancement analysis offer clear concise framework for understanding individuals’ emotional states using