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Bayesian Population Decoding of Motor Cortical Activity Using a Kalman Filter (2006)
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@MISC{Wu06bayesianpopulation,
author = {Wei Wu and Yun Gao and Elie Bienenstock and John P. Donoghue and Michael J. Black},
title = { Bayesian Population Decoding of Motor Cortical Activity Using a Kalman Filter},
year = {2006}
}
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Abstract
Effective neural motor prostheses require a method for decoding neural activity representing desired movement. In particular, the accurate reconstruction of a continuous motion signal is necessary for the control of devices such as computer cursors, robots, or a patient’s own paralyzed limbs. For such applications, we developed a real-time system that uses Bayesian inference techniques to estimate hand motion from the firing rates of multiple neurons. In this study, we used recordings that were previously made in the arm area of primary motor cortex in awake behaving monkeys using a chronically implanted multielectrode microarray. Bayesian inference involves computing the posterior probability of the hand motion conditioned on a sequence of observed firing rates; this is formulated in terms of the product of a likelihood and a prior. The likelihood term models the probability of firing rates given a particular hand motion. We found that a linear gaussian model could be used to approximate this likelihood and could be readily learned from a small amount
Keyphrases
motor cortical activity bayesian population decoding kalman filter hand motion arm area likelihood term model neural activity small amount multiple neuron primary motor cortex particular hand motion observed firing rate patient paralyzed limb effective neural motor prosthesis bayesian inference technique linear gaussian model bayesian inference real-time system posterior probability awake behaving monkey accurate reconstruction implanted multielectrode microarray computer cursor continuous motion signal
