title: Recognition of emotions using multimodal physiological signals and an ensemble deep learning model.
abstract: OBJECTIVE
  Using deep-learning methodologies to analyze multimodal physiological signals becomes increasingly attractive for recognizing human emotions. However, the conventional deep emotion classifiers may suffer from the drawback of the lack of the expertise for determining model structure and the oversimplification of combining multimodal feature abstractions.
  
  
  METHODS
  In this study, a multiple-fusion-layer based ensemble classifier of stacked autoencoder (MESAE) is proposed for recognizing emotions, in which the deep structure is identified based on a physiological-data-driven approach. Each SAE consists of three hidden layers to filter the unwanted noise in the physiological features and derives the stable feature representations. An additional deep model is used to achieve the SAE ensembles. The physiological features are split into several subsets according to different feature extraction approaches with each subset separately encoded by a SAE. The derived SAE abstractions are combined according to the physiological modality to create six sets of encodings, which are then fed to a three-layer, adjacent-graph-based network for feature fusion. The fused features are used to recognize binary arousal or valence states.
  
  
  RESULTS
  DEAP multimodal database was employed to validate the performance of the MESAE. By comparing with the best existing emotion classifier, the mean of classification rate and F-score improves by 5.26%.
  
  
  CONCLUSIONS
  The superiority of the MESAE against the state-of-the-art shallow and deep emotion classifiers has been demonstrated under different sizes of the available physiological instances.
authors.affiliation: Engineering Research Center of Optical Instrument and System, Ministry of Education, Shanghai Key Lab of Modern Optical System, University of Shanghai for Science and Technology, Shanghai, 200093, PR China. Electronic address: yinzhong@usst.edu.cn.
authors.forename: Zhong
authors.initials: Z
authors.lastname: Yin
authors.affiliation: School of Social Sciences, University of Shanghai for Science and Technology, Shanghai, 200093, PR China.
authors.forename: Mengyuan
authors.initials: M
authors.lastname: Zhao
authors.affiliation: Engineering Research Center of Optical Instrument and System, Ministry of Education, Shanghai Key Lab of Modern Optical System, University of Shanghai for Science and Technology, Shanghai, 200093, PR China. Electronic address: wyxiong@usst.edu.cn.
authors.forename: Yongxiong
authors.initials: Y
authors.lastname: Wang
authors.affiliation: Engineering Research Center of Optical Instrument and System, Ministry of Education, Shanghai Key Lab of Modern Optical System, University of Shanghai for Science and Technology, Shanghai, 200093, PR China.
authors.forename: Jingdong
authors.initials: J
authors.lastname: Yang
authors.affiliation: Department of Automation, East China University of Science and Technology, Shanghai 200237, PR China.
authors.forename: Jianhua
authors.initials: J
authors.lastname: Zhang
mesh_terms: D004644:Emotions; D006801:Humans; D007858:Learning; D008960:Models, Psychological
keywords: Affective computing; Deep learning; Emotion recognition; Ensemble learning; Physiological signals