Tracking the non-stationary neuron tuning by dual Kalman filter for brain machine interfaces decoding

Previous decoding approaches assume stationarity of the functional relationship between the neural activity and animal's movement in brain machine interfaces (BMIs). Studies show that the activity of individual neurons changes considerably from day to day. We propose to implement a dual Kalman structure to track neural tuning during the decoding process. While the kinematics are inferred as the state from the observation of neuron firing rates, the preferred direction of neuron tuning is also optimized by dual Kalman filtering on the linear coefficients of the observation model. When compared with the fixed tuning Kalman filter, the decoding performance of the adaptive dual Kalman filter is better (less Normalized Mean Square Error), which means that the evolving tuning of motor neuron is being tracked.

[1]  Eric A. Wan,et al.  Neural dual extended Kalman filtering: applications in speech enhancement and monaural blind signal separation , 1997, Neural Networks for Signal Processing VII. Proceedings of the 1997 IEEE Signal Processing Society Workshop.

[2]  Jerald D. Kralik,et al.  Real-time prediction of hand trajectory by ensembles of cortical neurons in primates , 2000, Nature.

[3]  M. Nicolelis,et al.  Reconstructing the Engram: Simultaneous, Multisite, Many Single Neuron Recordings , 1997, Neuron.

[4]  Yiwen Wang,et al.  Point process Monte Carlo filtering for brain machine interfaces , 2008 .

[5]  R E Kass,et al.  Recursive bayesian decoding of motor cortical signals by particle filtering. , 2004, Journal of neurophysiology.

[6]  Dawn M. Taylor,et al.  Extraction algorithms for cortical control of arm prosthetics , 2001, Current Opinion in Neurobiology.

[7]  José Carlos Príncipe,et al.  A Monte Carlo Sequential Estimation of Point Process Optimum Filtering for Brain Machine Interfaces , 2007, 2007 International Joint Conference on Neural Networks.

[8]  Deniz Erdogmus,et al.  Divide-and-conquer approach for brain machine interfaces: nonlinear mixture of competitive linear models , 2003, Neural Networks.

[9]  David M. Santucci,et al.  Learning to Control a Brain–Machine Interface for Reaching and Grasping by Primates , 2003, PLoS biology.

[10]  Wei Wu,et al.  Bayesian Population Decoding of Motor Cortical Activity Using a Kalman Filter , 2006, Neural Computation.

[11]  Jose C. Principe,et al.  Information Theoretical Analysis of Instantaneous Motor Cortical Neuron Encoding for Brain-Machine Interfaces , 2008 .

[12]  Deniz Erdogmus,et al.  Input-output mapping performance of linear and nonlinear models for estimating hand trajectories from cortical neuronal firing patterns , 2002, Proceedings of the 12th IEEE Workshop on Neural Networks for Signal Processing.

[13]  Nicholas G. Hatsopoulos,et al.  Brain-machine interface: Instant neural control of a movement signal , 2002, Nature.