Abstract
Near-infrared spectroscopy (NIRS) is a non-invasive neuroimaging technique that recently has been used to measure changes in cerebral blood oxygenation associated with brain activity. To date, there is no standard method for analyzing NIRS data, especially for real-time brain imaging applications. In this work, a novel real-time NIRS signal analysis framework based on the general linear model (GLM) and the Kalman estimator was devised. A set of simulated data was processed using the proposed framework. The results so obtained suggested that the method can effectively locate brain activation areas in real-time, thereby demonstrating its potential for real-time NIRS-based brain imaging applications.
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