# Review of Studies on Emotion Recognition and Judgment Based on Physiological Signals
###### Abstract
[8] presented a new self-supervised method that developed an approach to learn representations of individual physiological signals; the presented approach was more robust to losses in the input signal. [PERSON] et al. [9] adopted a bimodal structure to extract shared photoplethysmography and EMG to enhance recognition performance and obtained high performance of 80.18% and 75.86% for arousal and valence, respectively. [PERSON] et al. [10] presented a new deep learning-based method to extract and classify emotion-related information from the two-dimensional spectrograms obtained from EEG signals and offered a deep convolution neural network for emotion recognition with dense connections among layers to uncover the hidden deep features of EEG signals. The test results showed that the presented model boosted the emotion recognition accuracy by 8% compared to the state-of-the-art methods using the DEAP database. [PERSON] et al. [11] proposed a multimodal method for cross-subject emotion recognition based on the fusion of EEG and facial gestures and obtained the highest accuracy of 97.25% for valence and 96.1% for arousal, respectively. [PERSON] et al. [12] acquired a new EEG dataset (wireless-based EEG data for emotion analysis, WeDea) based on the discrete emotion theory and presented a new combination for WeDea analysis. The practical results indicated that WeDea was a promising resource for emotion analysis. [PERSON] et al. [13] presented a method with a framework from the machine learning literature, which was able to model time intervals by capturing the presence or absence of relevant states without the need to label the affective responses continuously. They indicated that the presented method showed reliability in a gold-standard scenario and towards real-world usage. [PERSON] et al. [14] presented a method of converting the DEAP dataset into a multimodal latent dataset for emotion recognition and applied the method to each participant's recordings to obtain a new latent encoded dataset. A naive Bayes classifier was used to assess the encoding performance of the 100-dimensional modalities, and compared to the original results, yielded leave-one-person-out cross-validation error rates of 0.3322 and 0.3327 for high/low arousal and valence states, while the original values were 0.349 and 0.382. In terms of new algorithms, [PERSON] et al. [15] built a substructure-based joint probability domain adaptation algorithm to overcome physiological signals' noise effects, and the new algorithm could avoid the weaknesses of domain level matching that was too rough and sample level matching that was susceptible to noise. [PERSON] et al. [16] used a new local mean decomposition algorithm to decompose EEG signals into product functions and even capture the underlying nonlinear characteristics of EEG; an emotion recognition system based on the presented algorithm outperformed state-of-the-art methods and achieved high accuracy. [PERSON] and [PERSON] [17] improved the performance of biosignal-based emotion and personality estimations by considering individual physiological differences as a covariate shift. They pointed out that importance weighting in machine learning models could reduce the effects of individual physiological differences in peripheral physiological responses. [PERSON] et al. [18] presented a new multi-filtering augmentation algorithm to increase the sample size of the ECG data. The algorithm augmented ECG signals by cleaning the data in different ways, and the benefit of the algorithm was measured using the classification accuracy of five machine learning algorithms. It was found that there was a significant improvement in performance for all of the datasets and classifiers.