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Emotion Recognition Based on Multichannel Physiological Signals with Comprehensive Nonlinear Processing

1
College of Intelligence and Computing, Tianjin University, Tianjin 300350, China
2
School of Computer Science and Engineering, Tianjin University of Technology, Tianjin 300384, China
3
Shenzhen Graduate School, Peking University, Shenzhen 518055, China
4
Department of Economics, Pennsylvania State University, State College, PA 16803, USA
*
Author to whom correspondence should be addressed.
Sensors 2018, 18(11), 3886; https://doi.org/10.3390/s18113886
Received: 13 September 2018 / Revised: 7 November 2018 / Accepted: 7 November 2018 / Published: 11 November 2018
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Abstract

Multichannel physiological datasets are usually nonlinear and separable in the field of emotion recognition. Many researchers have applied linear or partial nonlinear processing in feature reduction and classification, but these applications did not work well. Therefore, this paper proposed a comprehensive nonlinear method to solve this problem. On the one hand, as traditional feature reduction may cause the loss of significant amounts of feature information, Kernel Principal Component Analysis (KPCA) based on radial basis function (RBF) was introduced to map the data into a high-dimensional space, extract the nonlinear information of the features, and then reduce the dimension. This method can provide many features carrying information about the structure in the physiological dataset. On the other hand, considering its advantages of predictive power and feature selection from a large number of features, Gradient Boosting Decision Tree (GBDT) was used as a nonlinear ensemble classifier to improve the recognition accuracy. The comprehensive nonlinear processing method had a great performance on our physiological dataset. Classification accuracy of four emotions in 29 participants achieved 93.42%. View Full-Text
Keywords: emotion recognition; multichannel physiological signals; comprehensive nonlinear processing; KPCA and GBDT emotion recognition; multichannel physiological signals; comprehensive nonlinear processing; KPCA and GBDT
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Zhang, X.; Xu, C.; Xue, W.; Hu, J.; He, Y.; Gao, M. Emotion Recognition Based on Multichannel Physiological Signals with Comprehensive Nonlinear Processing. Sensors 2018, 18, 3886.

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