title: Higher-order Multivariable Polynomial Regression to Estimate Human Affective States.
abstract: From direct observations, facial, vocal, gestural, physiological, and central nervous signals, estimating human affective states through computational models such as multivariate linear-regression analysis, support vector regression, and artificial neural network, have been proposed in the past decade. In these models, linear models are generally lack of precision because of ignoring intrinsic nonlinearities of complex psychophysiological processes; and nonlinear models commonly adopt complicated algorithms. To improve accuracy and simplify model, we introduce a new computational modeling method named as higher-order multivariable polynomial regression to estimate human affective states. The study employs standardized pictures in the International Affective Picture System to induce thirty subjects' affective states, and obtains pure affective patterns of skin conductance as input variables to the higher-order multivariable polynomial model for predicting affective valence and arousal. Experimental results show that our method is able to obtain efficient correlation coefficients of 0.98 and 0.96 for estimation of affective valence and arousal, respectively. Moreover, the method may provide certain indirect evidences that valence and arousal have their brain's motivational circuit origins. Thus, the proposed method can serve as a novel one for efficiently estimating human affective states.
authors.affiliation: School of Electronic and Information Engineering, Southwest University, Chongqing, 400715, China.|Chongqing Key Laboratory of Non-linear Circuit and Intelligent Information Processing, Southwest University, Chongqing, 400715, China.
authors.forename: Jie
authors.initials: J
authors.lastname: Wei
authors.affiliation: School of Electronic and Information Engineering, Southwest University, Chongqing, 400715, China.|Chongqing Key Laboratory of Non-linear Circuit and Intelligent Information Processing, Southwest University, Chongqing, 400715, China.
authors.forename: Tong
authors.initials: T
authors.lastname: Chen
authors.affiliation: School of Electronic and Information Engineering, Southwest University, Chongqing, 400715, China.|Chongqing Key Laboratory of Non-linear Circuit and Intelligent Information Processing, Southwest University, Chongqing, 400715, China.
authors.forename: Guangyuan
authors.initials: G
authors.lastname: Liu
authors.affiliation: Faculty of Psychology, Southwest University, Chongqing, 400715, China.
authors.forename: Jiemin
authors.initials: J
authors.lastname: Yang
mesh_terms: D000339:Affect / Q000502:physiology*; D005712:Galvanic Skin Response; D006801:Humans; D008954:Models, Biological; D015999:Multivariate Analysis; D016571:Neural Networks, Computer; D017711:Nonlinear Dynamics; D010363:Pattern Recognition, Automated / Q000379:methods*; D010775:Photic Stimulation; D012044:Regression Analysis; D012815:Signal Processing, Computer-Assisted