Functional near-infrared spectroscopy (fNIRS) is a relatively new neuroimaging technique. It is a low cost, portable, and non-invasive method to measure brain activity via the BOLD signal. Similar to fMRI, it measures changes in the level of blood oxygen in the brain. Its time resolution is much finer than fMRI, however its spatial resolution is much courser. FNIRS is finding widespread use on young children whom cannot remain still in the MRI magnet and it can be used in situations where fMRI is contraindicated---such as with patients whom have cochlear implants. Furthermore, fNIRS measures the concentration of both oxygenated and deoxygenated hemoglobin, both of which are of scientific interest. In this talk, I propose a fully Bayesian time-varying AR model to analyze fNIRS data within the multivariate DLM framework. The hemodynamic response function is modeled with the canonical HRF and the low frequency drift with a variable B-spline model. Both the model error and the auto-regressive processes vary with time. Via simulation studies, I show that this model naturally handles motion artifacts and gives good statistical properties. The model is then apply to a fNIRS data set.