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Conf Proc IEEE Eng Med Biol Soc · Jan 2013
A flexible algorithm framework for closed-loop neuromodulation research systems.
- Dave Carlson, Dave Linde, Ben Isaacson, Pedram Afshar, Duane Bourget, Scott Stanslaski, Paul Stypulkowski, and Tim Denison.
- Conf Proc IEEE Eng Med Biol Soc. 2013 Jan 1;2013:6146-50.
AbstractModulation of neural activity through electrical stimulation of tissue is an effective therapy for neurological diseases such as Parkinson's disease and essential tremor. Researchers are exploring improving therapy through adjustment of stimulation parameters based upon sensed data. This requires classifiers to extract features and estimate patient state. It also requires algorithms to appropriately map the state estimation to stimulation parameters. The latter, known as the control policy algorithm, is the focus of this work. Because the optimal control policy algorithms for the nervous system are not fully characterized at this time, we have implemented a generic control policy framework to facilitate exploratory research and rapid prototyping of new neuromodulation strategies.
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