Description:
- Closed-loop brain stimulation for neurological disorders
Abstract
USC researchers have developed a technique to identify time-variant linear state-space models (SSMs), as well as an adaptive identification algorithm to calculate-variant SSMs and track non-stationarity in brain network dynamics. This method will allow for improved closed-loop electrical stimulation for common neurological disorders through the decoding of mood and other cognitive states.
Benefit
- Algorithm estimates time-variant SSMs that outperform time-invariant SSMs in predicting activity
- Successfully tracks non-stationarities
- Allows for accurate interpretation of mood and cognitive states
Market Application
Neurological disorders like epilepsy, Parkinson’s disease, and depression affect millions of people. To develop effective treatments like closed-loop electrical stimulation therapies for these disorders, accurate modeling of brain network activity is essential. However, the non- stationarity and time-variant dynamics of brain activity, especially during long-term monitoring using electrocorticogram (ECoG), pose significant challenges.
Publications
Generalized binary noise stimulation enables time-efficient identification of input-output brain network dynamics, Yang and Shanechi, 2016.
Other
Stage of Development