Selecting neural subsets for kinematics decoding by information theoretical analysis in motor Brain Machine Interfaces

Yiwen Wang, Justin C. Sanchez, Jose C. Principe

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

4 Scopus citations

Abstract

Previous decoding algorithms for Brain Machine Interfaces (BMIs) reconstruct the kinematics from recorded activities of hundreds of neurons, which are not all related to the movement task. Decoding from all neurons not only brings problem towards model generalization but also a significant computation burden. Knowledge of neural receptive fields helps ascertain the neuron importance associate with the movements. We propose to apply information theoretical analysis based on an instantaneous tuning model to extract the candidate neuron subsets, which also reduces the computation complexity for the decoding process. The cortical distribution of extracted neuron subsets is analyzed and the statistical decoding performances using neuron subset selection are compared to the one by the full neuron ensemble.

Original languageEnglish (US)
Title of host publication2009 International Joint Conference on Neural Networks, IJCNN 2009
Pages3275-3280
Number of pages6
DOIs
StatePublished - Nov 18 2009
Event2009 International Joint Conference on Neural Networks, IJCNN 2009 - Atlanta, GA, United States
Duration: Jun 14 2009Jun 19 2009

Publication series

NameProceedings of the International Joint Conference on Neural Networks

Other

Other2009 International Joint Conference on Neural Networks, IJCNN 2009
CountryUnited States
CityAtlanta, GA
Period6/14/096/19/09

ASJC Scopus subject areas

  • Software
  • Artificial Intelligence

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    Wang, Y., Sanchez, J. C., & Principe, J. C. (2009). Selecting neural subsets for kinematics decoding by information theoretical analysis in motor Brain Machine Interfaces. In 2009 International Joint Conference on Neural Networks, IJCNN 2009 (pp. 3275-3280). [5178809] (Proceedings of the International Joint Conference on Neural Networks). https://doi.org/10.1109/IJCNN.2009.5178809