A square root ensemble Kalman filter application to a motor-imagery brain-computer interface

We here investigated a non-linear ensemble Kalman filter (SPKF) application to a motor imagery brain computer interface (BCI). A square root central difference Kalman filter (SR-CDKF) was used as an approach for brain state estimation in motor imagery task performance, using scalp electroencephalography (EEG) signals. Healthy human subjects imagined left vs. right hand movements and tongue vs. bilateral toe movements while scalp EEG signals were recorded. Offline data analysis was conducted for training the model as well as for decoding the imagery movements. Preliminary results indicate the feasibility of this approach with a decoding accuracy of 78%–90% for the hand movements and 70%–90% for the tongue-toes movements. Ongoing research includes online BCI applications of this approach as well as combined state and parameter estimation using this algorithm with different system dynamic models.

[1]  E. Harth,et al.  Electric Fields of the Brain: The Neurophysics of Eeg , 2005 .

[2]  P. Nunez,et al.  Electric fields of the brain , 1981 .

[3]  Rudolph van der Merwe,et al.  The square-root unscented Kalman filter for state and parameter-estimation , 2001, 2001 IEEE International Conference on Acoustics, Speech, and Signal Processing. Proceedings (Cat. No.01CH37221).

[4]  G Pfurtscheller,et al.  Frequency component selection for an EEG-based brain to computer interface. , 1999, IEEE transactions on rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society.

[5]  Kazufumi Ito,et al.  Gaussian filters for nonlinear filtering problems , 2000, IEEE Trans. Autom. Control..

[6]  Jeffrey K. Uhlmann,et al.  New extension of the Kalman filter to nonlinear systems , 1997, Defense, Security, and Sensing.

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

[8]  Carl Erik Fröberg,et al.  Introduction to Numerical Analysis , 1969 .

[9]  Febo Cincotti,et al.  Relevant EEG features for the classification of spontaneous motor-related tasks , 2002, Biological Cybernetics.

[10]  R. Srinivasan.  Methods to Improve the Spatial Resolution of EEG , 1999 .

[11]  Greg Welch,et al.  Welch & Bishop , An Introduction to the Kalman Filter 2 1 The Discrete Kalman Filter In 1960 , 1994 .

[12]  H.F. Durrant-Whyte,et al.  A new approach for filtering nonlinear systems , 1995, Proceedings of 1995 American Control Conference - ACC'95.

[13]  Wei Wu,et al.  Neural Decoding of Cursor Motion Using a Kalman Filter , 2002, NIPS.

[14]  M. Kamrunnahar,et al.  Toward a Model-Based Predictive Controller Design in Brain–Computer Interfaces , 2011, Annals of Biomedical Engineering.

[15]  Niels Kjølstad Poulsen,et al.  New developments in state estimation for nonlinear systems , 2000, Autom..

[16]  Michael J. Black,et al.  Closed-loop neural control of cursor motion using a Kalman filter , 2004, The 26th Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[17]  M Congedo,et al.  A review of classification algorithms for EEG-based brain–computer interfaces , 2007, Journal of neural engineering.

[18]  R. E. Kalman,et al.  A New Approach to Linear Filtering and Prediction Problems , 2002 .

[19]  Rudolph van der Merwe,et al.  Sigma-point kalman filters for probabilistic inference in dynamic state-space models , 2004 .

[20]  P. Nunez,et al.  Source analysis of EEG oscillations using high-resolution EEG and MEG. , 2006, Progress in brain research.

[21]  Rudolph van der Merwe,et al.  Efficient derivative-free Kalman filters for online learning , 2001, ESANN.

[22]  Dean J Krusienski,et al.  Brain-computer interface signal processing at the Wadsworth Center: mu and sensorimotor beta rhythms. , 2006, Progress in brain research.

[23]  S M M Martens,et al.  A generative model approach for decoding in the visual event-related potential-based brain–computer interface speller , 2010, Journal of neural engineering.

[24]  S. Julier,et al.  A General Method for Approximating Nonlinear Transformations of Probability Distributions , 1996 .

[25]  T. Başar,et al.  A New Approach to Linear Filtering and Prediction Problems , 2001 .