A parallel point-process filter for estimation of goal-directed movements from neural signals
Author(s)Modir Shanechi, Maryam; Wornell, Gregory W.; Williams, Ziv; Brown, Emery N.
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Brain machine interfaces work by mapping the relevant neural activity to the intended movement known as 'decoding'. Here, we develop a recursive Bayesian decoder for goal-directed movements from neural observations, which exploits the optimal feedback control model of the sensorimotor system to build better prior state-space models. These controlled state models depend on the movement duration that is not known a priori. We thus consider a discretization of the task duration and develop a decoder consisting of a bank of parallel point-process filters, each combining the neural observation with the controlled state model of a discretization point. The final reconstruction is made by optimally combining these filter estimates. Using very coarse discretization and hence only a few parallel branches, our decoder reduces the root mean square (RMS) error in trajectory reconstruction in reaches made by a rhesus monkey by approximately 40%.
DepartmentHarvard University--MIT Division of Health Sciences and Technology; Massachusetts Institute of Technology. Department of Brain and Cognitive Sciences; Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
Proceedings of the 2010 IEEE International Conference on Acoustics Speech and Signal Processing (ICASSP)
Institute of Electrical and Electronics Engineers
Shanechi, Maryam Modir et al. “A Parallel Point-process Filter for Estimation of Goal-directed Movements from Neural Signals.” IEEE, 2010. 521–524. Web. © 2010 IEEE.
Final published version
INSPEC Accession Number: 11553666