| Summary: | There is a growing demand for emotion recognition systems (ERS) to be adopted in everyday life from various fields, particularly automotive, education, and social security. Recently, the use of cardio-based physiological signals, electrocardiogram (ECG), and photoplethysmogram (PPG) in ERS has yielded promising results. Furthermore, the development of wearable devices equipped with cardio-based physiological sensors has significantly aided towards the adoption of ERS in daily life. This paper systematically reviews emotion recognition using cardio-based physiological signals, encompassing emotion models, emotion elicitation methods, and ERS development methods, emphasizing feature extraction, feature selection methods, feature dimension reduction methods, and classifiers. A summary and comparison of recent studies are presented to highlight existing studies’ gaps and suggest future research for better ERS especially using cardio-based signals.
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