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In order to investigate the human emotional state recognition, the functional near-infrared spectroscopy (fNIRs) technique is applied to measure hemodynamic signals of 15 participants who are requested to see six types of pictures, and the participants have to complete 7-point rating scale of valence and arousal after every picture stimulus. The support vector machine (SVM) and support vector machine based recursive feature elimination (SVM-RFE) algorithm are applied to design classifiers. Under different emotional image stimulus, the hemodynamic signals of some participants show significant neural response. With the target classification based on valence, arousal and emotion category, the accuracy is 81%, 78.78% and 68%, respectively. The 5 th and 6 th channels for fNIRs measurement are significantly sensitive to arousal and valence state, and the two channels are located at orbitonfrontal cortex and dorsolateral prefrontal cortex regions. Besides, it is found that the entropy of fNIRs can reflect the variation in emotional state effectively. The results suggest that fNIRs can be used for recognition of human emotional state.

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