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Project Overview
- Neuroprosthetic is an example of neuroengineering
resulted from the application of the knowledge in decoding
the brain signals to assist paraplegic patients to
move by volitional control signals recorded from the
brain.
Rationale
- The neural code used in voluntary movement execution
is known in neuroscience as "population vector
code."
- Arm movement trajectory can be predicted based on
the the population vector code of a large ensemble
of neurons recorded in the motor cortex.
- With the ability to decode the motor-command signal
in the motor cortex, arm movement direction and velocity
can be specified based on the neural code.
- Paralyzed patients will be able to move a robot arm
based on the neural code extracted from the neurons
in the motor cortex.
Research Objectives
- Primates are used as the experimental model to test
these neuroengineering techniques to make voluntary
arm movements by "thinking about it" without
actual execution of arm movements by making vitrual
3-D arm movements via virtual-reality display feedback
fitted in front of the animal. [Primate research is
done by Dr. Andrew Schwartz at the University of Pittsburgh.
Neural spike train data are provided by Dr. Schwartz.]
- The goal is to develop real-time decoding algorithms
to extract information from the implanted multi-electrode
array in the motor cortex to drive the robotic arm.
Specific Goals
- The neural code used in voluntary movement execution
is known in neuroscience as "population vector
code."
- Arm movement trajectory can be predicted based on
the the population vector code of a large ensemble
of neurons recorded in the motor cortex.
The Challenge
- Real-time implementation of the algorithm is essential.
It is computationally expensive to analyze a hundred
channels of neural data simultaneously. Efficient computational
algorithms are needed. Off-line analysis can be performed
without any constraints on the real-time performance.
But if the neuroprosthesis were to be practical, real-time
responses are essential.
- Incomplete data set. Ideally, there should be enough
data points in the neural recordings to compute the
predicted arm trajectory. In reality, the firing rates
of the neurons are very low in the motor cortex. This
puts constriants on the statistical sampling size problem
to extract enough information about the intended trajectory
to drive the robot arm. Ingenious methods using adaptive
predictive algorithms can overcome these limitations.
The Solutions
- Adaptive algorithm is used to "learn" from
prior movement trials to adjust for the unknown coefficients
of the parameters needed to generate the predictive
movement trajectory.
- Predictive algorithm is used to "project" what
the future neural code would be before the actual code
are filled in from the neural recordings. This allows
us to overcome the incomplete data sample problem.
- Adaptive-corrective algorithm allows us to correct
the on-going errors from the estimations, and adjust
the predictive algorithm accordingly.
- See publication: Tam, D. C. (2003) Real-Time Estimation
of Predictive Firing Rate. Neurocomputing, 52-54: 637-641.
[Reprint.pdf]
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