Exploring New Horizons: fNIRS and Machine Learning in Understanding PostCOVID-19

PostCOVID-19 is a condition affecting approximately 10% of individuals infected with SARS-CoV-2, presenting significant challenges in diagnosis and clinical management. Portable neuroimaging techniques, such as functional near-infrared spectroscopy (fNIRS), offer real-time insights into cerebral hemodynamics and represent a promising tool for studying postCOVID-19 in naturalistic settings. This study investigates the integration of fNIRS with machine learning to identify neural correlates of postCOVID-19. A total of six machine learning classifiers—Random Forest, Support Vector Machine (SVM), K-Nearest Neighbors (KNNs), XGBoost, Logistic Regression, and Multi-Layer Perceptron (MLP)—were evaluated using a stratified subject-aware cross-validation scheme on a dataset comprising 29,737 time-series samples from 37 participants (9 postCOVID-19, 28 controls). Four different feature representation strategies were compared: raw time-series, PCA-based dimensionality reduction, statistical feature extraction, and a hybrid approach that combines time-series and statistical descriptors. Among these, the hybrid representation demonstrated the highest discriminative performance. The SVM classifi