Sample-Efficient Reinforcement Learning Controller for Deep Brain Stimulation in Parkinson's Disease

Document Type

Conference Proceeding

Publication Date

1-1-2025

Abstract

Deep brain stimulation (DBS) is an established intervention for Parkinson's disease (PD), but conventional open-loop systems lack adaptability, are energy-inefficient due to continuous stimulation, and provide limited personalization to individual neural dynamics. Adaptive DBS (aDBS) offers a closed-loop alternative, using biomarkers such as beta-band oscillations to dynamically modulate stimulation. While reinforcement learning (RL) holds promise for personalized aDBS control, existing methods suffer from high sample complexity, unstable exploration in binary action spaces, and limited deployability on resourceconstrained hardware. We propose SEA-DBS, a sample-efficient actor-critic framework that addresses the core challenges of RL-based adaptive neurostimulation. SEA-DBS integrates a predictive reward model to reduce reliance on real-Time feedback and employs Gumbel-Softmax-based exploration for stable, differentiable policy updates in binary action spaces. Together, these components improve sample efficiency, exploration robustness, and compatibility with resource-constrained neuromodulatory hardware. We evaluate SEA-DBS on a biologically realistic simulation of Parkinsonian basal ganglia activity, demonstrating faster convergence, stronger suppression of pathological beta-band power, and resilience to post-Training FP16 quantization. Our results show that SEA-DBS offers a practical and effective RL-based aDBS framework for real-Time, resource-constrained neuromodulation.

Publication Title

Proceedings 2025 IEEE International Conference on Intelligent Mobile Computing Mobilecloud 2025

ISBN

[9798331589172]

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