# Review of Studies on Emotion Recognition and Judgment Based on Physiological Signals
###### Abstract  
People's emotions play an important part in our daily life and can not only reflect psychological and physical states, but also play a vital role in people's communication, cognition and decision-making. Variations in people's emotions induced by external conditions are accompanied by variations in physiological signals that can be measured and identified. People's psychological signals are mainly measured with electroencephalograms (EEGs), electrodermal activity (EDA), electrocardiograms (ECGs), electromyography (EMG), pulse waves, etc. EEG signals are a comprehensive embodiment of the operation of numerous neurons in the cerebral cortex and can immediately express brain activity. EDA measures the electrical features of skin through skin conductance response, skin potential, skin conductance level or skin potential response. ECG technology uses an electrocardiogram to record changes in electrical activity in each cardiac cycle of the heart from the body surface. EMG is a technique that uses electronic instruments to evaluate and record the electrical activity of muscles, which is usually referred to as myoelectric activity. EEG, EDA, ECG and EMG have been widely used to recognize and judge people's emotions in various situations. Different physiological signals have their own characteristics and are suitable for different occasions. Therefore, a review of the research work and application of emotion recognition and judgment based on the four physiological signals mentioned above is offered. The content covers the technologies adopted, the objects of application and the effects achieved. Finally, the application scenarios for different physiological signals are compared, and issues for attention are explored to provide reference and a basis for further investigation. emotion; recognition; classification; judgment; EEG; EDA; ECG; EMG; review +
Footnote †: journal: Medical and Design, Hangzhou Dianzi University, Hangzhou 310018, China  
more truly reflect human emotions. Human emotion recognition and judgment systems are also based on the physiological and behavioral signals. Compared with the system based on behavioral signals, the system based on physiological signals has the advantages of high time resolution and convenient measurement, so the identification of emotions from physiological signals has extensive applications in many fields such as education, industrial design, medicine, rehabilitation, entertainment, pedagogy, psychology, etc. [3], thus attracting widespread attention. In the last two years, some achievements including new models, new approaches or methods, new algorithms, and new systems have emerged. In terms of new models, [PERSON] et al. [4] presented a multimodal emotion recognition model based on manifold learning and a convolutional neural network, and the test results showed that the presented model achieved high identification accuracy. [PERSON] et al. [5] employed a self-supervised learning paradigm to make it possible to learn representations directly from unlabeled signals and subsequently use them to classify affective states, and the test showed that the self-supervised representations could learn widely useful features that improve data efficiency and do not require the data to be labeled for learning. [PERSON] et al. [6] presented a new model to study how augmented reality systems were effective in distinguishing the emotional states of students engaged in book reading activities with EEG. They indicated that augmented reality-based reading had a significant discriminatory effect and achieved higher classification performance than real reading, and the presented model was good at classifying emotional states from EEG signals, with accuracy scores close to 100%. [PERSON] [7] presented a new feedback control schema to manipulate the user state based on user-virtual reality system interaction and conducted real experiments with a virtual reality system prototype to show the effectiveness of the presented schema. In terms of new approaches or methods, [PERSON] et al.