Selecting neural subsets for kinematics decoding by information theoretical analysis in motor brain machine interfaces

Authors:
Yiwen Wang;Justin C. Sanchez;Jose C. Principe
Affiliations:
Electrical and Computer Engineering Department, University of Florida, Gainesville, FL;Department of Electrical and Computer Engineering, University of Florida, Gainesville, FL;Department of Pediatrics, University of Florida, Gainesville, FL
Venue:
IJCNN'09 Proceedings of the 2009 international joint conference on Neural Networks
Year:
2009

Citing 2
Cited 1

Divide-and-conquer approach for brain machine interfaces: nonlinear mixture of competitive linear models

Neural Networks - 2003 Special issue: Advances in neural networks research — IJCNN'03
Bayesian Population Decoding of Motor Cortical Activity Using a Kalman Filter

Neural Computation

2009 Special Issue: Ascertaining neuron importance by information theoretical analysis in motor Brain-Machine Interfaces

Neural Networks

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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.