Is Mate Preference Recognizable Based on Electroencephalogram Signals? Machine Learning Applied to Initial Romantic Attraction

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.

Motivation in the metaverse: A dual-process approach to consumer choices in a virtual reality supermarket

“…However, our results showed a contrast in alpha frontal asymmetry, in which there was a relatively higher right frontal activity in the no-choice phase. For the beta and gamma frequency bands, there was a relatively higher frontal asymmetry during the “choice” conditions, which was in line with previous research that relates higher left frontal beta activity compared to right frontal activity to consumers’ purchase decisions ( Aftanas et al, 2006 ; Ramsøy et al, 2018 ; Zhao et al, 2018 ; Alarcão and Fonseca, 2019 ; Lin et al, 2021 ; Yuan et al, 2022 ).…”

A portable affective computing system for identifying mate preference

“…Recent studies have found that the preferences of marriageable individuals towards potential romantic partners is associated with a decrease in the power of alpha and beta frequency bands [7], as well as an increase in the amplitude of LPP and P300 [8]. Based on these findings, Yuan et al (2021) extracted relevant features from EEG signals and conducted research on romantic interest recognition using a random forest classifier, with an average classification accuracy of 85.2% [9].…”