# Review of Studies on Emotion Recognition and Judgment Based on Physiological Signals
## 2 Research and Application of EEG  
Changes in people's emotions comprise a sophisticated process and often lead to spatio-temporal brain activity that can be captured with EEG. The EEG signal is rich in useful information because it is a comprehensive embodiment of the operation of numerous neurons in the cerebral cortex and can immediately reflect brain activity. In addition, the EEG signal is simple to record and cost-effective. So, the EEG-based method has attracted widespread attention and is broadly used for emotion recognition and judgment. [PERSON] et al. [24] put forward an approach to emotion image classification according to the user's experience measured with EEG signals and eye movements, and built a relationship between the psychological signals and the expected emotional experience. They showed that the proposed approach was accurate in distinguishing images that could cause a pleasurable experience by classifying 16 abstract art paintings as positive, negative or neutral according to volunteers' physiological responses. [PERSON] and [PERSON] [25] studied emotion recognition and judgment based on EEG signals. They defined emotions as two-level and three-level classes with valence and arousal dimensions, extracted features using fast Fourier transform, and performed feature selection with the Pearson correlation coefficient. The results showed that the mean precision for the valence and arousal estimation was 70.9% and 70.1%, respectively, for the two-level class, and 55.4% and 55.2%, respectively, for the three-level class. [PERSON] et al. [26] let game players play \"League of Legends\" and imaged players' brain activity through functional near infrared spectroscopy as well as recording videos of the players' faces. Based on the above, they decoded the expertise level of the players in a multi-modal framework, which was the first work decoding expertise level of players with non-restrictive technology for brain imaging. They declared that the best tri-class classification precision was 91.44%. [PERSON] et al. [27] first measured the EEG signals and extracted features from the signals, then processed the EEG data using a modified algorithm of the radial basis function neural network, and finally compared and discussed the experimental results given by different classification models. The results showed that the modified algorithm was better than other algorithms. [PERSON] et al. [28] proposed a new approach to preference-based measurement based on user's aesthetic preferences using EEG for a moving virtual three-dimensional object. The EEG signals were classified into two classes of like and dislike, and the classification accuracy could reach 80%. [PERSON] et al. [29] put forward a complete acquisition protocol to establish a physiological emotional database for subjects, and then formulated arousal assessment as 2 or 3 degrees. The results confirmed the possibility of applying EEG signals to evaluate the arousal component of emotion. [PERSON] and [PERSON] [30] employed various classifiers to categorize the EEG signal into three emotional states and used the discrete wavelet transform to decompose EEG signals into different frequency bands. They also derived time domain features from the EEG signal to recognize different emotions. The results indicated that the maximum classification rates were 71.52% and 60.19%, respectively, when the classification methods of decision tree and k nearest neighbor were used, and the higher frequency spectrum performed well in emotion recognition. [PERSON] et al. [31] presented a new approach to recognize emotions with a series of multi-channel EEG signals by changing the EEG signals into a sequence of multi-spectral topology images. Based on the new approach, they obtained testing accuracy of 86.13% for arousal, 90.62% for valence, 88.48% for dominance, and 86.23% for like-unlike. [PERSON] et al.