Identifying emotional valence from FMRI data

Abstract

Identifying emotional valence from fMRI data is a significant step toward understand- ing how the brain processes affective states. This has important implications in fields such as cognitive neuroscience, mental health, and affective computing. However, decoding emotional valence using machine learning remains a challenging task, espe- cially due to the high dimensionality of fMRI data, inter-subject variability, and the subtle nature of emotional responses. Achieving high classification accuracy is partic- ularly difficult, making it crucial to explore effective feature extraction methods. This study focuses on comparing the efficiencyof two popular featureengineering approaches— General Linear Model (GLM) -based methods and Independent Com- ponent Analysis (ICA) —for emotion classification using fMRI data. Specifically, we implement the GLMSingle method for GLM-based feature extraction and compare it against ICA-derived features. The extracted features are evaluated using multiple ma- chine learning classifiers, including Support Vector (SVM) , Gaussian Naive Bayes (GNB), RandomForest(RF),and LogisticRegression(LR).Weassessclassification performance both within subjects and across subjects to evaluate generalizability. The results show that features derived from the GLMSingle method consistently outperform those from ICA in terms of classification accuracy. This suggests that task-related modeling using GLM provides more discriminative features for emotion prediction than the data-driven ICA approach. The findings highlight the importance of choosing suitable feature engineering strategies in order to improve model perfor- mance when decoding affective states from neuroimaging data.