The study introduces a novel approach, ϵ-Neural Thompson Sampling, to optimize deep brain stimulation frequency. It outperforms traditional methods and shows promise in balancing exploration and exploitation. The research focuses on improving treatment outcomes for Parkinson's disease through innovative adaptive strategies.
Deep brain stimulation (DBS) is effective for Parkinson's disease but faces limitations with fixed parameters. Recent research explores adaptive DBS using reinforcement learning and contextual bandits. The proposed ϵ-NeuralTS algorithm balances exploration and exploitation to enhance treatment efficacy.
The study evaluates the ϵ-NeuralTS algorithm using a computational model of basal ganglia activity in PD patients' brains. Results show improved performance over existing methods, emphasizing the potential of adaptive DBS techniques. The research aims to advance automation in parameter selection for DBS controllers.
Key metrics such as Error Index (EI) and Beta-band Power Spectral Density (Pβ) are used to evaluate the effectiveness of different stimulation frequencies. The study highlights the importance of context-based approaches in optimizing DBS therapy for Parkinson's disease.
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by Hao-Lun Hsu,... at arxiv.org 03-12-2024
https://arxiv.org/pdf/2403.06814.pdfDeeper Inquiries