Functional Near-Infrared Spectroscopy Feature Extraction with Application in Workload Estimation

Functional near-infrared spectroscopy (fNIRS) is a brain imaging technique used to estimate neuronal activity by measuring blood oxygenation. In this paper, we develop and evaluate an extensive set of fNIRS features for workload estimation, combining them with respiration and heartbeat signals. Our subject- and session-independent workload estimator is validated in a virtual flight simulator, where workload is objectively assessed based on task performance. We experiment with various regression models and feature ablations, identifying the most effective fNIRS features. The best fNIRS-based model achieves a correlation of 0.3188 with objective workload labels, improving to 0.3268 when incorporating breathing signals. This study demonstrates the value of our novel fNIRS feature set for workload estimation.