Imali Thanuja Hettiarachchi1, Shady Mohamed2, Saeid Nahavandi2
1Centre for Intelligent Systems Research, Deakin University, Geelong, VIC , Australia; 2Centre for Intelligent Systems Research, Deakin University, Geelong, VIC, Australia
This work demonstrates a novel Bayesian learning approach for model based analysis of Functional Magnetic Resonance (fMRI) data. We use a physiologically inspired hemodynamic model and investigate a method to simultaneously infer the neural activity together with hidden state and the physiological parameter of the model. This joint estimation problem is still an open topic. In our work we use a Particle Filter accompanied with a kernel smoothing approach to address this problem within a general filtering framework. Simulation results show that the proposed method is a consistent approach and has a good potential to be enhanced for further fMRI data analysis.