Development of a Small, Low-Power Real-Time Phase-Dependent Neuromodulation System

Neurological diseases and neuropsychiatric disorders are often characterized by abnormal neural oscillations, such as exaggerated synchronization or suppression within a narrow frequency band and complex oscillation coupling which disrupt normal brain function and contribute to debilitating symptoms. Phase-dependent stimulation (PDS) offers a promising solution by synchronizing electrical stimulation with specific phases of neural oscillations, thereby enhancing therapeutic precision and efficacy. However, the widespread clinical adoption of PDS is hindered by technological challenges, including the need for accurate detection and prediction of neural oscillatory phases in real-time, stimulation management, stimulus artifact removal, fast communication, and adaptable hardware for dynamic neural environments. This study aims to address some of these challenges by leveraging adaptive System-on-Chip and Field-Programmable Gate Array (FPGA) technology, which offers the computational power and flexibility required for real-time neural signal processing and management. Specifically, we propose to optimize, integrate, and validate our PDS technique within this advanced hardware framework to develop a unified, closed-loop phase-dependent neuromodulation system. We evaluated our devices performance by assessing its latency and accuracy in targeting specific phases of stimulation on both simulated signals and intraoperative cortical and subcortical recordings. Our findings indicate that the device successfully sent stimulation commands in time with the occurrence of target phases with both high accuracy and low latency for extended time periods. This work has the potential to transform therapeutic approaches for disorders with well-described brain network dysfunction, offering a precise, adaptable, and safer alternative to traditional stimulation techniques.