Supervised information gain-based feature selection for multimodal physiological signals in stress prediction

Feature selection plays a critical role in identifying the most informative signals across multiple modalities in physiological datasets. While correlation-based approaches have traditionally been used to discover relationships among features, they are often unsupervised and do not incorporate class label information, limiting their discriminative power in supervised classification tasks. In this work, we propose a supervised, Information Gain (IG)-based feature selection framework for multimodal data acquired from wrist-worn sensors, including Blood Volume Pulse (BVP), Acceleration (ACC), Skin Temperature (TEMP), and Electrodermal Activity (EDA).Time–frequency feature representations are extracted from each modality using Short-Time Fourier Transform (STFT) and subsequently ranked according to their correlation with class labels using Information Gain(IG) criterion. The top-ranked features for each modality are then fused into a compact, high-quality representation for classification using Random Forest classifier. Experiments on the WESAD stress recognition dataset demonstrate that our proposed STFT-IG framework along with Random Forest classifier achieves competitive performance under subject-independent evaluation using the Leave-One-Subject-Out (LOSO) protocol. Furthermore, comparative analyses with alternative feature selection methods and feature representations are performed to contextualize the effectiveness of the proposed approach.