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Recognition of odor and pleasantness based on olfactory EEG combined with functional brain network model

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Abstract

In the food field, the sensory evaluation of food texture, aroma, and flavor still relies on artificial sensory evaluation and machine perception, but the results of artificial sensory evaluation by professionals are not universal, and machines cannot obtain psychological information. In this work, an electroencephalogram (EEG) analysis method based on the functional brain network was proposed, which effectively realized odor recognition and pleasantness recognition. Firstly, a self-developed odor generator was used to induce olfactory EEG, and the signals were collected and preprocessed. Secondly, the functional brain networks were constructed by mutual information (MI). Finally, the network properties were extracted and input to the support vector machine (SVM) classifier. Compared with the traditional EEG feature extraction methods, the degree of the functional brain network can effectively extract EEG features, the average accuracy and F1 score of odor recognition were 95.78% and 95.24%, respectively, and in the pleasantness recognition were 98.21% and 98.21%, respectively. In conclusion, a method for odor and pleasantness recognition was proposed, which provided a new idea for food sensory evaluation.

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Data availability

The data that support the findings of this study are available on request from the corresponding author upon reasonable request. The data are not publicly available due to privacy or ethical restrictions.

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Funding

This work was supported by the National Natural Science Foundation of China [31772059], and the Science and Technology Development Plan of Jilin Province [YDZJ202101ZYTS135].

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Correspondence to Yan Shi or Hong Men.

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Xia, X., Liu, X., Zheng, W. et al. Recognition of odor and pleasantness based on olfactory EEG combined with functional brain network model. Int. J. Mach. Learn. & Cyber. 14, 2761–2776 (2023). https://doi.org/10.1007/s13042-023-01797-7

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