Abstract
Effective features are the key to emotion recognition of physiological signals. In this paper, we analyze 140 data samples of different emotions which are acquired from 20 subjects by using curve fitting function model to fit the rising edge of the GSR (galvanic skin response) physiological signal, and then we extract the emotional features of GSR signal from the fitting parameters and their derivative variables to distinguish different emotions. In the classification test of selected features, the feature normalized was used to recognize four kinds of emotions, and the best correct-recognition rate of one-to-one and one-to-more classification could reach 91.43% and 88.40% respectively.
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