# Review of Studies on Emotion Recognition and Judgment Based on Physiological Signals
## 2 Research and Application of EEG
[32] proposed a magnetoencephalogram-based database for decoding affective user responses that, different from data sets, had little contact with the scalp and thus promoted a naturalistic affective response and enabled fine-grained analysis of cognitive reactions over brain lobes, in turn favoring emotion recognition. [PERSON] et al. [33] presented an EEG-based art therapy evaluation approach that could assess therapeutic effectiveness according to the variation in emotion before and after therapy. The results indicated that the model of emotion recognition with long short-term memory deep temporal features had better classification effects than the state-of-the-art approach with non-temporal features, and the classification accuracy in the high-frequency band was higher than that in the low-frequency band. [PERSON] et al. [34] rebuilt EEG phase space and converted it into a new state space, and extracted the Poincare intersections as features and then fed them into the classification model. They declared that not only was the presented approach effective in emotion recognition, but it also introduced a new method for nonlinear signal processing. [PERSON] and [PERSON] [35] proposed a machine learning-based approach to discern stress patterns induced by driving with EEG signals, and built relations between brain dynamics and emotion. They also presented a framework to identify emotions based on EEG patterns by recognizing specific features of the emotion from the raw EEG signal. Fifty subjects were tested, and high classification accuracy, precision, sensitivity and specificity were obtained. [PERSON] et al. [36] analyzed the results of people's physiological estimation under different illumination levels and color temperatures based on EEG signals. The results showed that illumination greatly affected the response of the visual center, which was conducive to the design of light environments. In order to recognize and classify fear levels based on EEG and databases for emotion analysis, [PERSON] et al. [37] compared different machine and deep learning methods with and without selecting features, and found that, although all methods could give high classification accuracy, the highest F scores were obtained using the random forest classifier--89.96% as shown in Table 1 and 85.33% as shown in Table 2 for the two-level fear (0--no fear and 1--fear) and four-level fear (0--no fear, 1--low fear, 2--medium fear, 3--high fear) evaluation modalities, respectively. In Tables 1 and 2, F1 is fear, DNN is deep neural network, SCN is stochastic configuration network, SVM is support vector machine, RF is random forest, LDA is linear discriminant analysis, and KNN is k-nearest neighbors. [PERSON] et al. [40] proposed a new method to recognize emotion through unprocessed EEG signals, and it was found that, with the new method, the classification accuracy for arousal, valence and dominance could be higher than 88%. [PERSON] et al. [41] presented an assessment approach based on EEG signals. They first determined the brain cognition laws by evaluating the EEG power topographic map under the effect of three types of automobile sound, i.e., the qualities of comfort, powerfulness, and acceleration, then classified the EEG features thus recognized as different automobile sounds, and finally employed the Kalman smoothing and minimal redundancy maximal relevance algorithm to modify the accuracy and reduce the amount of calculation. The results indicated that there existed differences in the neural features of different automobile sound qualities. [PERSON] et al. [42] studied the issue of inducing and checking driving anger. They first collected data on brain signals of driving anger when drivers encountered three typical scenarios that could lead to driving anger, and then distinguished normal driving from angry driving according to the self-reported data; finally, a classifier was used to detect angry driving based on the features, and 85.0% detection accuracy was obtained.