Personalized Emotion Prediction Models Based on Individual Neural and Physiological Signals

Mental health disorders affect approximately 970 million people globally, representing 1 in every 8 individuals, with anxiety and depressive disorders being the most common. Poor mental health can interfere with daily activities, work performance, and physical health lower overall quality of life. The growing prevalence of people suffering from mental health issues has led to a significant demand for accurate emotion prediction tools that can enhance mental health diagnostics and provide personalized user experiences. Human emotion prediction largely relies on facial expression analysis, which has limitations due to the potential for hiding or misinterpretation. According to the previous studies, these challenges was addressed by focusing on physiological signals as they offer a more direct and reliable way to assess the emotional states of human emotions based on actual reactions rather than outward expressions. Combining complex data types, such as neural signals from the brain and physiological signals like breathing, can significantly improve the prediction accuracy, but it presents a considerably challenge due to the diverse input. To address these challenges, we propose a novel approach that utilizes dimensionality normalization techniques to standardize multimodal inputs, combining neural data from fMRI with physiological signals such as PPG and respiration. Our methodology integrates four machine learning models—Random Forest, Support Vector Machine, Gradient Boosting, and K-Nearest Neighbors—to optimize emotion prediction accuracy. We validated our model on a dataset of 600 trials from 20 participants, achieving a classification accuracy of 85% for valence classes (positive, negative, and neutral) and strong performance in predicting valence ratings (scale -4 to 4). The results underscore the effectiveness of our approach in advancing emotion prediction, potentially leading to significant improvements in mental health care, personalized technology, and user experience design.