Can your brain signals reveal your romantic emotions?

• Published in: Computers in biology and medicine Vol. 196; no. Pt A; p. 110754
• Main Authors: Zazon, Dor, Nissim, Nir
• Format: Journal Article
• Language: English
• Published: United States Elsevier Ltd 01.09.2025
• Subjects: Adult; Brain - physiology; EEG; Electroencephalography; Emotions - physiology; Evoked Potentials - physiology; Female; Humans; Internal Medicine; Machine learning, deep learning; Male; Mobile Applications; Other; Romantic attraction; Romantic rejection; Single-trial ERP analysis; Young Adult; Romantic attraction; Machine learning, deep learning; Single-trial ERP analysis; Romantic rejection; EEG
• ISSN: 0010-4825; 1879-0534; 1879-0534
• DOI: 10.1016/j.compbiomed.2025.110754

The process of partner selection may result in emotions of romantic attraction when one expresses interest towards a potential partner, and rejection when one receives negative feedback from a potential partner. Previous EEG studies have found distinct neural correlates for both emotions in the context of dating apps. However, to the best of our knowledge, no study has demonstrated the ability to predict the associated intra-subject romantic emotions based on a single-trial analysis of event related potential (ERP). In this study, 61 participants (31 females and 30 males) agreed to use our simulated dating app, and their EEG brain activity was recorded during their engagement with the app. Based on each participant's EEG signals, we induced multiple machine and deep learning models aimed at predicting single-trial romantic attraction and rejection for each participant. Our results show that the best model obtained 71.38 % and 81.31 % average ROC-AUC scores across the participants respectively for romantic attraction and rejection. We also found that our learning models were able to predict romantic emotions more accurately for picky participants than they could for those that were less fussy, which might suggest that picky people have stronger brain activity signals when it comes to romantic preference. •We present a method for predicting romantic attraction and rejection from EEG signals.•The prediction is done based on EEG single-trial using advanced data science methods.•The best model obtained 71.38 % averaged AUC score for romantic attraction.•The best model obtained 81.31 % averaged AUC score for romantic rejection.•Our method was able to predict romantic feelings more accurately for picky participants.