Abstract
Initial romantic attraction (IRA) refers to a series of positive reactions toward potential ideal partners based on individual preferences; its evolutionary value lies in facilitating mate selection. Although the EEG activities associated with IRA have been preliminarily understood; however, it remains unclear whether IRA can be recognized based on EEG activity. To clarify this, we simulated a dating platform similar to Tinder. Participants were asked to imagine that they were using the simulated dating platform to choose the ideal potential partner. Their brain electrical signals were recorded as they viewed photos of each potential partner and simultaneously assessed their initial romantic attraction in that potential partner through self-reported scale responses. Thereafter, the preprocessed EEG signals were decomposed into power-related features of different frequency bands using a wavelet transform approach. In addition to the power spectral features, feature extraction also accounted for the physiological parameters related to hemispheric asymmetries. Classification was performed by employing a random forest classifier, and the signals were divided into two categories: IRA engendered and IRA un-engendered. Based on the results of the 10-fold cross-validation, the best classification accuracy 85.2% (SD = 0.02) was achieved using feature vectors, mainly including the asymmetry features in alpha (8–13 Hz), beta (13–30 Hz), and theta (4–8 Hz) rhythms. The results of this study provide early evidence for EEG-based mate preference recognition and pave the way for the development of EEG-based romantic-matching systems.
Highlights
Finding an ideal partner is a prerequisite for achieving high-quality romantic relationships
Numerous studies have demonstrated that the random forest classifier (RFC) performs well in preference classification tasks based on EEG signals; in this study, the RFC was used to classify and detect the users’ Initial romantic attraction (IRA) toward potential partners based on features obtained through TF analyses
The classification performance of the proposed EEG-based mate preference recognition algorithm was verified using a total of 2878 EEG samples collected from 50 participants
Summary
Finding an ideal partner is a prerequisite for achieving high-quality romantic relationships. Physiological signals have many advantages over self-reported data, one of which is that they are less susceptible to subjective consciousness and environmental factors (Lin et al, 2010; Alarcao and Fonseca, 2019) These signals open up new possibilities for identifying users’ emotional responses and preferences for potential partners. Zhang et al (2021) successfully identified participants’ initial romantic interest to potential partners based on the features extracted from electrocardiogram signals, while Lu et al (2020) successfully detected participants’ initial romantic desire to potential romantic partners based on the information extracted from photoplethysmogram signals These results demonstrate that IRA, as an important part of human emotion, can be recognized on the basis of periphery physiological signals (Lu et al, 2020; Zhang et al, 2021). Numerous studies have demonstrated that the random forest classifier (RFC) performs well in preference classification tasks based on EEG signals; in this study, the RFC was used to classify and detect the users’ IRA toward potential partners based on features obtained through TF analyses
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