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Our lab seeks to understand neuronal patterns in the central nervous system in both health and in disease. We apply a variety of computational techniques to model and modulate neuronal behaviors including mechanistic modeling, statistical modeling, dynamical systems and control techniques. We often work with multivariate time series data (e.g. EEG) and apply machine learning techniques to uncover patterns in neuronal data. Our applications span Parkinson's disease, epilepsy, brain-machine interfaces, chronic pain, decision making, and sleep.
Deep brain stimulation (DBS), a neurosurgical treatment that stimulates the brain with electrical signals, is used to treat Parkinson’s disease (PD) and an ever-increasing list of neurological disorders. Despite growing numbers of applications, DBS is at a relative technological standstill due to several factors: limited choice of stimulus waveforms, ability to stimulate at only a single location, and inefficient use of battery power.
Journal Articles (published)
- [J1] Vyas S, Huang H, Gale JT, and Sarma SV*, Montgomery Jr EB. (2015) Neuronal Complexity in Subthalamic Nucleus is Reduced in Parkinson's Disease. IEEE Trans Neural Syst Rehabil Eng. Volume:PP Issue:99. (*co-senior author)
- [J2] Czanner G, Sarma SV, Ba D, Eden UT, Wu W, Eskandar E, Lim HH, Temereanca S, Suzuki WA, and Brown EN. (2015) Measuring the signal-to-noise ratio of a neuron. Proc Natl Acad Sci U S A. vol.
- Sarma SV, Subramanian S., Hao S. Computational tool for pre-surgical evaluation of patients
with medically refractory epilepsy. Patent (filed October 2012)
- Sarma SV. Quickest Detection on Dependent Data: Application to Seizure Prediction in Epilepsy Patients. Patent (filed January 2012)
- Sarma SV, Brown EN, Eskandar E, System and Method for Dynamically Configurable Deep Brain Stimulation. Patent Number 8521294.