Classification of fNIRS data with LDA and SVM: a proof-of-concept for application in infant studies

This study presents the implementation of a within-subject classification method, based on the use of Linear Discriminant Analysis (LDA) and Support Vector Machines (SVM), for the classification of hemodynamic responses. Using a synthetic dataset that closely resembles real experimental infant functional near-infrared spectroscopy (fNIRS) data, the impact of different levels of noise and different HRF amplitudes on the classification performances of the two classifiers are quantitively investigated.