A Monte Carlo Sequential Estimation for Point Process Optimum Filtering

Yiwen Wang, A.R.C. Paiva, J.C. Principe
2006 The 2006 IEEE International Joint Conference on Neural Network Proceedings  
The previous decoding algorithms for Brain Machine Interfaces are normally utilized to estimate animal's movement from binned spike rates, which loses spike timing resolution and may exclude rich neural dynamics due to single spikes. Based on recently proposed Monte Carlo sequential estimation algorithm on point process, we present a decoding framework to reconstruct the kinematic states directly from the multi-channel spike trains. Starting with analysis on the differences between the
more » ... n and real BMI data, neural tuning properties are modeled to encode the movement information of the experimental primate as the pre-knowledge for Monte-Carlo sequential estimation for BMI. The preliminary kinematics reconstruction shows better results when compared with Kalman filter.
doi:10.1109/ijcnn.2006.246904 dblp:conf/ijcnn/WangPP06 fatcat:hbxhobp65rfrtm4com32huanj4