# Review of Studies on Emotion Recognition and Judgment Based on Physiological Signals
## 3 Research and Application of EDA, ECG and EMG
The ECG data based on physiological characteristics are directly related to an individual, and hence often used to identify a person's psychological state. To improve the ability of emotional recognition from ECG signals, [PERSON] et al. [52] presented a wavelet scattering algorithm to extract the features of signals based on the AMIGOS database as inputs for different classifiers evaluating their performance, and reported that an accuracy of 88.8%, 90.2% and 95.3% had been obtained in the valence, arousal and two-dimensional classification, respectively, using the presented algorithm. [PERSON] et al. [53] applied ECG features including time-frequency domain, waveform and non-linear characteristics, combined with their presented model of emotion recognition, to recognize a driver's calm and anxiety. Accuracy scores of 91.34% and 92.89% for calm and anxiety, respectively, were obtained. [PERSON] and [PERSON] [54] used ECG to study experimentally  
Figure 1: System of virtual gesture control and a manipulator propelled by emotion [48]. Figure 2: Steering change driven by emotions [48]. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & \multicolumn{2}{c}{**JAWS**} & \multicolumn{2}{c}{**JAWS\_pos**} & \multicolumn{2}{c}{**JAWS\_neg**} \\ \cline{2-6}  & \multicolumn{2}{c}{**Without**} & \multirow{2}{*}{**With PCA (\%)**} & \multicolumn{2}{c}{**Without**} & \multirow{2}{*}{**With PCA (\%)**} & \multirow{2}{*}{**With PCA (\%)**} & \multirow{2}{*}{**With PCA (\%)**} & \multirow{2}{*}{**With PCA (\%)**} \\  & **PCA (\%)** & & & & & \\ \hline ACC & 65.85 & 73.17 & 60.89 & 73.17 & 63.41 & 78.05 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Comparison of the division of the participants according to the classifier [51]. the impact of music on emotions. The results showed that fast, intermediate and slow music strengthened, inhibited and did not change the activity of the autonomic sympathetic nervous system, respectively. They also suggested that music could be used to relieve psychological pressure. [PERSON] et al. [55] presented an approach using the human heart rate as a new form of modality to determine human emotions with a physiological mouse measuring photoplethysmographic data and experimentally evaluated the accuracy of the approach. [PERSON] and [PERSON] [56] collected 140 signal samples of ECG triggered by Self-Assessment Manikin emotion self-assessment experiments using the International Affective Picture System, and used Wasserstein generative adversarial network with gradient penalty to add different numbers of samples for different classes. The results showed that the accuracy and weighted F1 values of all three classifiers were improved after increasing the quantity of data. EMG is a technique using electronic instruments to evaluate and record the electrical activity of muscles, which is usually referred to as myoelectric activity. There are two kinds of methods for EMG: one is the surface derivation method, i.e., the method of attaching an electrode to the skin to derive the potential; the other is the needle electrode method, which is the method of inserting a needle electrode into a muscle to derive the local potential. Automatic analysis of EMG can be performed by using computer technology, such as analytical EMG, single-fiber EMG and giant EMG. [PERSON] et al. [57] studied whether identification could be modified by focusing subjects on their internal reactions, and tested how changes in expression type and presentation parameters affect facial expression and recognition rate based on EMG. They found that the participants had different EMG responses, i.e., maximal corrugator responses to angry, then neutral, and minimum to happy faces.