# Bandit Algorithms for Deep Brain Stimulation

arXiv: https://arxiv.org/abs/2601.12699 · HTML: https://mlsanity.com/paper/2601.12699/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 5 of its 32 references that are in the map.
- First posted on arXiv 19 Jan 2026.

## Builds on (most cited first, 5 of 5)

- [Conservative Contextual Linear Bandits](https://mlsanity.com/paper/1611.06426/) (2016)
- [Offline Learning of Closed-Loop Deep Brain Stimulation Controllers for Parkinson Disease Treatment](https://mlsanity.com/paper/2302.02477/) (2023)
- [WAND: A 128-channel, closed-loop, wireless artifact-free neuromodulation device](https://mlsanity.com/paper/1708.00556/) (2017)
- [ε-Neural Thompson Sampling of Deep Brain Stimulation for Parkinson Disease Treatment](https://mlsanity.com/paper/2403.06814/) (2024)
- [In-Vivo Training for Deep Brain Stimulation](https://mlsanity.com/paper/2510.03643/) (2025)

## Abstract (arXiv)

Deep Brain Stimulation (DBS) is an effective treatment for Parkinson's disease, but conventional fixed-parameter stimulation can reduce battery life and cause side effects while failing to adapt to changing neural dynamics. Recent reinforcement learning approaches improve adaptability, yet most rely on deep neural networks that require offline training and are computationally too expensive for implantable hardware. This paper presents a resource-conscious adaptive DBS framework based on a Time- and Threshold-Triggered Pruned Multi-Armed Bandit (T3P MAB) algorithm. The proposed method jointly tunes stimulation frequency and amplitude, avoids prior training, and remains transparent enough to support clinician-guided adjustment. Using a computational basal ganglia-thalamic model, we show that T3P converges faster than competing MAB methods and outperforms deep-RL baselines in suppressing pathological beta-band activity while reducing stimulation power. We implemented it on different microcontrollers and report detailed energy measurements, showing convergence in under two minutes and suitability for resource-constrained implantable systems. These results support lightweight bandit-based control as a practical path toward personalized, energy-efficient DBS.