## 2 Related Work  
Changes in physiological signals can be influenced by human emotions, and since the proposal of non-invasive devices that can collect human physiological signals in real-time, many efforts have been made to analyze physiological signals. First, public datasets, DEAP [21], SEED (2015) [22], AMIGOS [23], etc., are proposed, and then a series of sentiment computation models are proposed to analyze them. [PERSON] [24] studied the mental arousal space in four quadrants and solved a four-category task using the graph regularization extreme learning machine (GELM) method, these two obtained about 70% accuracy in the polynomial classification task study. When data are incomplete, semi-supervised learning methods can be used to integrate Stack Auto-Encoder (SAE) with deep belief networks (DBN) using a decision fusion method and based on Bayesian inference classification [25], yielding 73.1% accuracy in arousal and 78.8% accuracy in valence. Another recent GSR-based framework ref. [26] proposed temporal and spectral features of SVM (RBF kernel) under the AMIGOS dataset, reporting 83.9% and 65% arousal and valence recognition accuracy, respectively. New trends in emotion-evoking computing use deep neural networks (DNNs) to process physiological signals and improve recognition rates. One of the earliest attempts was [27], which proposed a multimodal residual LSTM for emotion recognition (MMResLSTM) yielded encouraging results, with their classification accuracy of 92.87% for arousal and 92.30% for valence on the DEAP dataset. Ref. [28] processed ECG and GSR data from the AMIGOS dataset and proposed to use machine learning methods and DCNN to process the data, obtained better results of 0.76 for valence and 0.75 for arousal. A recent study Yang [29] presented the fusion of statistical features extracted from EEG, ECG, and GSR of the AMIGOS dataset. They reported recognition rates of 67% and 68.8% for valence and arousal, respectively, which using the SVM classifier. LSTM-RNN was recently proposed [30] using an attention-based mechanism for the AMIGOS dataset and reported recognition rates of 79.4% and 83.3% for binary classification of valence and arousal. Four-category emotion results also became progressively more common, however, the reported recognition rates decreased more in the case of four categories of emotions [31]. [PERSON] [32] proposed a one-dimensional convolutional neural network model to analyze ECG and GSR signals in the AMIGOS dataset with an accuracy of 65.25% for the A-V four-category emotion recognition task. The features extracted from physiological signals are the most important aspect in emotion recognition. The processing is carried out in the time domain, frequency domain, or nonlinear domain. Time domain methods include the use of various mathematical/statistical features such as mean [33], median, etc., or the use of methods such as sample differences, zero-crossing, etc. In the frequency domain, the Fourier transform (FT) [34] and the wavelet transform [35] are widely used. The FT allows one to use time-based features on the signal (e.g., its mean or DC component and dominant frequency component) represented in the spectrum. The nonlinear domain approaches require the conversion to the sensor signals to discrete symbolic strings, and the key to performing this conversion is the discretization process. Once these signals are mapped to strings, exact or approximate matches and edit distances [36]. Compared three feature selection algorithms Joint Mutual Information (JMI), Conditional Mutual Information Maximization (CMIM), and Dual Input Symmetric Correlation (DISR) on the AMIGOS dataset and concluded that the three feature selection algorithms are similar, and it is important to have the same number of features to obtain the best accuracy for arousal recognition and valence recognition. Therefore, which features are better, need to be further explored.