Brain machine interfaces (BMIs) have the potential to restore movement to patients experiencing paralysis. Although great progress has been made towards BMIs there is still much work to be done. This dissertation addresses some of the problems associated with the signal processing side of BMIs.

Since neural communication within the brain is still unknown, probabilistic modeling is argued as the best approach for BMIs. Specifically, generative models are proposed with hidden variables to help model the multiple interacting processes (both hidden and observable). Some of the advantages of the generative models over the conventional BMI signal processing algorithms are also confirmed. This includes the modeling of inhibited neurons and the ability to separate the neural input space. The partitioning of the input neural space is based on the hypothesis that animals transition between neural state structures during goal seeking. These neural structures are analogous to the motion primitives or ‘movemes’, exhibited during the kinematics. This leads to a paradigm shift similar to a divide and conquer methodology but with generative models. The generative models are also used to cluster the neural input space. This is appropriate since the desired kinematic data is not available from paralyzed patients. Most BMI algorithms ignore this very important point.

The results are justified with the improvement in trajectory reconstruction. Specifically, the correlation coefficient on the trajectory reconstruction serves as a metric to compare against other BMI methods. Additionally, simulations are used to show the models’ ability to cluster unknown data with underlying dependencies. This is necessary since there are no ground truths in real neural data.