# Review of Studies on Emotion Recognition and Judgment Based on Physiological Signals
## 4 Research on and Application of Multimodal Physiological Signals
### Multiple Physiological Signals  
[PERSON] et al. [65] assessed human perceptions by recording subjects' EMG, EMG and galvanic skin response signals while neutral, smiling and frowning faces as well as eight images of ceramic flooring were projected. The results indicated that the forms of ceramic tile flooring could be distinguished by values of galvanic skin response, and there was a sharp distinction in the EMG signals induced by the calibration images. [PERSON] et al. [66] presented a regularized deep fusion framework for use in emotion recognition based on the physiological signals of EEG, EMG, galvanic skin response and respiratory rate. They extracted effective features from the above signals and built ensemble-dense embeddings of multimodal features from which they applied a deep network framework to learn task-specific representations for each signal, and then designed a global fusion layer with a regularization term to blend the produced representations. There results showed that the presented framework could modify the effect of emotion recognition, and the blended representation could exhibit higher class separability for emotion recognition. [PERSON] et al. [67] evaluated the reliability of physiological variations produced by six different basic emotions with measurements of physiological signals of skin conductance level, blood volume pulse and fingertip temperature when 12 subjects watched emotion-provoking film clips and self-assessed their emotions under the stimuli in the films. It was found that the physiological signals obtained in the emotion-arousing period displayed good internal consistency. [PERSON] et al. [68] presented an approach to assess, analyze, and fix human emotions using signals of EEG, ECG, galvanic skin response, photoplethysmograms and respiration. Using the presented approach, six different emotional states could be recognized, and high accuracy performance was obtained. [PERSON] et al. [69] presented a new adaptive approach to modify the system of emotion recognition with measurements of skin conductance, blood volume pressure and inter-beat intervals along with three-channel forehead biosignals as emotional modalities. Six different emotions were stimulated by showing video clips to each of the 25 subjects. The results indicated that the presented approach was better than conventional methods, and overall emotion classification accuracy of 84.7% and 80% was obtained, respectively, using support vector machine and K-nearest neighbors classifications. [PERSON] et al. [70] studied experimentally whether auditory stimulation could be used as a stimulus source for emotion judgment by recording signals of human EEG, facial EMG, EDA and respiration. The results showed that auditory stimulation was as effective as visual stimulation. They also presented culture-specific, i.e., Chinese vs. Indian, prediction models, and no major differences in prediction accuracy were observed between the presented culture-specific models. [PERSON] et al. [71] first proposed an approach using hybrid feature extraction to extract statistical- and event-related features from ECG, EDA, EMG and blood volume pulse, then presented a method of adaptive decision fusion to integrate signal modalities for emotion classification, and finally assessed the presented framework through a comparative analysis based on a dataset. The results showed that it was necessary to use event-related features and emphasized the significance of improving the adaptive decision fusion method for emotion classification. [PERSON] et al. [72] developed an E-learning system endowed with emotion computing ability for the cultivation of relational skills based on 10 different kinds of emotions detected from the facial expressions, vocal nonverbal features and postures of 34 subjects. [PERSON] et al. [73] developed a machine with the ability to recognize daily eight emotional states from EMG, EDA, blood volume pressure and respiration signals over multiple weeks.