A General and Scalable Vision Framework for Functional Near-Infrared Spectroscopy Classification

Functional near-infrared spectroscopy (fNIRS), a non-invasive optical technique, is widely used to monitor brain activities for disease diagnosis and braincomputer interfaces (BCIs).Deep learning-based fNIRS classification faces three major barriers: limited datasets, confusing evaluation criteria, and domain barriers.We apply more appropriate evaluation methods to three openaccess datasets to solve the first two barriers.For domain barriers, we propose a general and scalable vision fNIRS framework that converts multi-channel fNIRS signals into multi-channel virtual images using the Gramian angular difference field (GADF).We use the framework to train state-of-the-art visual models from computer vision (CV) within a few minutes, and the classification performance is competitive with the latest fNIRS models.In crossvalidation experiments, visual models achieve the highest average classification results of 78.68% and 73.92% on mental arithmetic and word generation tasks, respectively.Although visual models are slightly lower than the fNIRS models on unilateral finger-and foot-tapping tasks, the F1score and kappa coefficient indicate that these differences are insignificant in subject-independent experiments.Furthermore, we study fNIRS signal representations and the classification performance of sequence-to-image methods.We hope to introduce rich achievements from the CV domain to improve fNIRS classification research.