A Monte Carlo sequential estimation of point process optimum filtering for brain machine interfaces

Yiwen Wang, António R.C. Paiva, José C. Príncipe, Justin C. Sanchez

Research output: Chapter in Book/Report/Conference proceedingConference contribution

4 Scopus citations

Abstract

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 simulation 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.

Original languageEnglish (US)
Title of host publicationThe 2007 International Joint Conference on Neural Networks, IJCNN 2007 Conference Proceedings
Pages2250-2255
Number of pages6
DOIs
StatePublished - Dec 1 2007
Event2007 International Joint Conference on Neural Networks, IJCNN 2007 - Orlando, FL, United States
Duration: Aug 12 2007Aug 17 2007

Publication series

NameIEEE International Conference on Neural Networks - Conference Proceedings
ISSN (Print)1098-7576

Other

Other2007 International Joint Conference on Neural Networks, IJCNN 2007
CountryUnited States
CityOrlando, FL
Period8/12/078/17/07

ASJC Scopus subject areas

  • Software

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