Description:
- Deep brain stimulation for conditions including Parkinson's disease, epilepsy, and major depression
Abstract
USC researchers have developed a technique for efficiently identifying input-output dynamics in brain networks to improve closed-loop stimulation systems and BMIs. Utilizing a Generalized Binary Noise (GBN) modulated stimulation pattern, the method incorporates time-constant information to achieve rapid identification. In tests of a closed-loop controller in a clinical stimulation system targeting depression symptoms, the GBN-based controller performed as well as an ideal controller with full network model knowledge in just 20 minutes, and it outperformed a controller based on traditional Binary Noise identification. These findings hold significant promise for optimizing system identification and closed-loop control of brain network dynamics.
Benefit
- Efficient identification of IO dynamics in brain networks
- Rapid system identification using GBN modulated stimulation pattern
- Improved closed-loop control of network dynamics
Market Application
Millions of patients suffer from conditions like Parkinson's disease, epilepsy, and major depression. Current treatments, such as deep brain stimulation, are conducted in an open-loop and ad-hoc manner, resulting in inefficiencies and imprecision. By developing model-based and closed-loop stimulation systems or brain-machine interfaces (BMIs), neurological disorders can be treated more efficiently and effectively.
Publications
Generalized binary noise stimulation enables time-efficient identification of input-output brain network dynamics, Yang and Shanechi, 2016.
Other
Stage of Development
- Experimentally validated
- Available for licensing