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. 2024 Jul 31;14(1):17735.
doi: 10.1038/s41598-024-68772-2.

A portable affective computing system for identifying mate preference

Guangjie Yuan  1   2 , Tao Wang  3 , Wei Ju  3 , Sai Fu  4
Affiliations

A portable affective computing system for identifying mate preference

Guangjie Yuan et al. Sci Rep. .

Abstract

Recognizing an individual's preference state for potential romantic partners based on electroencephalogram (EEG) signals holds significant practical value in enhancing matchmaking success rates and preventing romance fraud. Despite some progress has been made in this field, challenges such as high-dimensional feature space and channel redundancy limited the technology's practical application. The aim of this study is to explore the most discriminative EEG features and channels, in order to enhance the recognition performance of the system, while maximizing the portable and practical value of EEG-based systems for recognizing romantic attraction. To achieve this goal, we first conducted an interesting simulated dating experiment to collect the necessary data. Next, EEG features were extracted from various dimensions, including band power and asymmetry index features. Then, we introduced a novel method for EEG feature and channel selection that combines the sequential forward selection (SFS) algorithm with the frequency-based feature subset integration (FFSI) algorithm. Finally, we used the random forest classifier (RFC) to determine a person's preference state for potential romantic partners. Experimental results indicate that the optimal feature subset, selected using the SFS-FFSI method, attained an average classification accuracy of 88.42%. Notably, these features were predominantly sourced from asymmetry index features of electrodes situated in the frontal, parietal, and occipital lobes.

Keywords: Cognitive neuroscience; Feature selection; Interpersonal attraction; Machine learning; Mate preference; Romantic attraction.

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Conflict of interest statement

The authors declare no competing interests.

Figures

Figure 1
Figure 1
Experimental protocol.
Figure 2
Figure 2
Trial structure.
Figure 3
Figure 3
Framework for obtaining the OFS in the proposed feature selection method.
Figure 4
Figure 4
weight map of the OFS.
Figure 5
Figure 5
OFS and its spatial distribution position.
Figure 6
Figure 6
Classification accuracy of mate preference based on different feature selection methods.
See this image and copyright information in PMC

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