An Improved Beta Burst Extraction for Chip-Based Deep Brain Stimulation With Real-Time Model Updating.

Abstract

Goal: Existing beta burst detection algorithms for closed-loop deep brain stimulation (DBS) are computationally complex, limiting their use in implantable devices. We aimed to develop an improved beta burst extraction algorithm for chip-based DBS devices with real-time model updating. Methods: Building on an established beta burst detection method, we proposed a sliced mechanism for peak frequency finding and modified burst extraction for information sharing with real-time model updating. Results: Testing on rat electrocorticographic (ECoG) recordings showed that the proposed algorithm maintains a strong correlation ( 0.89 0.06) with the conventional method, with a 53.3 reduction in computational complexity for peak frequency finding. Conclusions: Integrating this improved beta burst detection into chip-based DBS devices represents a key algorithmic advancement toward adaptive neuromodulation therapies. The strong correlation and reduced complexity validate our proposal for real-time neural biomarker tracking, facilitating hardware and chip implementation, and advancing the development of implantable systems.