Real-time functional near infrared spectroscopy based brain activation strength estimation

Functional near-infrared spectroscopy (fNIRS) is a non-invasive neuroimaging technique that recently has been used for brain imaging applications. To date, there is no standard method for analyzing fNIRS data, especially for real-time applications. In this work, a novel real-time fNIRS data analysis framework based on the general linear model (GLM) and the Kalman estimator was devised. A pilot study was conducted by applying the proposed framework to a set of data recorded in a finger tapping experiment. The results so obtained suggested that the method can effectively estimate the brain activity strength of the detected areas in real-time, thereby demonstrating its potential for real-time fNIRS-based brain imaging applications.