Title :
Computing nonlinear features of skin conductance to build the affective detection model
Author :
Jing Cheng ; Guangyuan Liu
Author_Institution :
Sch. of Comput. & Inf. Sci., Southwest Univ., Chongqing, China
Abstract :
Skin conductance (SC) is one of most important physiological signal, which has been proven to contain reliable affective information. In the paper three kinds of affective status are induced by movie clips in laboratory environments, which are happiness, sadness and fear, and the corresponding affective SC signal is collected by Biopac MP150. After preprocessing the original SC signal, several nonlinear features are computed, which include largest Lyapunov exponent, correlation dimension, approximate entropy, and Hurst exponent. Finally based on these features the classifier SVM is used to setup model of affect detection. The experimental results show it is feasible to build affective model based on SC´s nonlinear features.
Keywords :
Lyapunov methods; bioelectric potentials; entropy; medical signal detection; medical signal processing; signal classification; skin; support vector machines; Biopac MP150; Hurst exponent; Lyapunov exponent; SC signal preprocessing; SVM classifier; affect detection; affective detection model; approximate entropy; correlation dimension; fear; happiness; nonlinear features; physiological signal; sadness; skin conductance; support vector machine; Correlation; Entropy; Feature extraction; Motion pictures; Physiology; Support vector machines; Time series analysis;
Conference_Titel :
Communications, Circuits and Systems (ICCCAS), 2013 International Conference on
Conference_Location :
Chengdu
Print_ISBN :
978-1-4799-3050-0
DOI :
10.1109/ICCCAS.2013.6765349
