Deep Brain Stimulation (DBS) is a treatment for Parkinson’s Disease that sends targeted electrical impulses to specific brain areas to manage motor symptoms. Unlike conventional systems that provide constant stimulation without adapting to real-time brain activity, closed-loop DBS adjusts stimulation based on brain signals. However, current systems that rely on beta band activity (13-30 Hz) face challenges such as: i) dependence on the length of the recorded signal, ii) reduced temporal resolution, iii) high computational load, and, in some patients, poor or absent detectability of activity in the beta band.
The proposed solution involves the use of an innovative method designed to dynamically adapt stimulation based on the intrinsic features of neural signals, effectively addressing the limitations of conventional strategies.
Preliminary data show that this technology is more effective than beta-band-based approaches in discriminating stimulation and medication states, paving the way for significant improvements in closed-loop DBS systems.