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 blood oxygen level dependent 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--similar to EEG or MEG. 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 scientifc interest. In this talk, I propose a fully Bayesian time-varying autoregressive model to analyze fNIRS data via a hybrid Bayesian multivariate DLM model. The hemodynamic response function is modeled with the canonical HRF and the low frequency drift with a variable B-spline model (both locations and number of knots are allowed to vary). The auto-regressive process varies with time while the data errors are modeled using a mixture model to account for outliers. 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.