An empirical study of emotion recognition using physiological signals

Emotion recognition plays a crucial role in various fields, including psychology, human-computer interaction, and affective computing. In recent years, there has been a growing interest in using physiological signals to accurately and non-invasively detect and classify human emotions. This paper presents a comprehensive review of the current state-of-the-art techniques and advancements in emotion recognition using physiological signals. The review begins by introducing the importance of emotion recognition and the challenges associated with traditional methods, such as self-reporting and facial expressions analysis. This paper provides an overview of different emotion recognization techniques like a self-assessment report, facial expression, speech, body gestures, textual data and physiological signals. It then focuses on the utilization of physiological signals, including electrocardiography (ECG), electroencephalography (EEG), galvanic skin response (GSR), electromyography (EMG), Respiration, and skin temperature.