Laboratory for Neuroengineering, Department of Health Science and Technology, Institute for Robotics and Intelligent Systems, ETH Zürich, 8092, Zurich, Switzerland.
J Neuroeng Rehabil. 2023 Sep 26;20(1):131. doi: 10.1186/s12984-023-01246-0.
The identification of the electrical stimulation parameters for neuromodulation is a subject-specific and time-consuming procedure that presently mostly relies on the expertise of the user (e.g., clinician, experimenter, bioengineer). Since the parameters of stimulation change over time (due to displacement of electrodes, skin status, etc.), patients undergo recurrent, long calibration sessions, along with visits to the clinics, which are inefficient and expensive. To address this issue, we developed an automatized calibration system based on reinforcement learning (RL) allowing for accurate and efficient identification of the peripheral nerve stimulation parameters for somatosensory neuroprostheses.
We developed an RL algorithm to automatically select neurostimulation parameters for restoring sensory feedback with transcutaneous electrical nerve stimulation (TENS). First, the algorithm was trained offline on a dataset comprising 49 subjects. Then, the neurostimulation was then integrated with a graphical user interface (GUI) to create an intuitive AI-based mapping platform enabling the user to autonomously perform the sensation characterization procedure. We assessed the algorithm against the performance of both experienced and naïve and of a brute force algorithm (BFA), on 15 nerves from five subjects. Then, we validated the AI-based platform on six neuropathic nerves affected by distal sensory loss.
Our automatized approach demonstrated the ability to find the optimal values of neurostimulation achieving reliable and comfortable elicited sensations. When compared to alternatives, RL outperformed the naïve and BFA, significantly decreasing the time for mapping and the number of delivered stimulation trains, while improving the overall quality. Furthermore, the RL algorithm showed performance comparable to trained experimenters. Finally, we exploited it successfully for eliciting sensory feedback in neuropathic patients.
Our findings demonstrated that the AI-based platform based on a RL algorithm can automatically and efficiently calibrate parameters for somatosensory nerve stimulation. This holds promise to avoid experts' employment in similar scenarios, thanks to the merging between AI and neurotech. Our RL algorithm has the potential to be used in other neuromodulation fields requiring a mapping process of the stimulation parameters.
ClinicalTrial.gov (Identifier: NCT04217005).
神经调节的电刺激参数的确定是一个针对个体且耗时的过程，目前主要依赖于用户（例如临床医生、实验者、生物工程师）的专业知识。由于刺激参数会随时间变化（由于电极移位、皮肤状况等），患者需要进行反复的、长时间的校准，同时还需要到诊所就诊，这既低效又昂贵。为了解决这个问题，我们开发了一种基于强化学习（RL）的自动化校准系统，用于准确高效地确定用于体感神经假肢的周围神经刺激参数。
我们开发了一种 RL 算法，用于自动选择经皮神经电刺激（TENS）恢复感觉反馈的神经刺激参数。首先，该算法在包含 49 名受试者的数据集上进行离线训练。然后，将神经刺激与图形用户界面（GUI）集成，创建一个直观的基于 AI 的映射平台，使用户能够自主执行感觉特征描述过程。我们在五名受试者的 15 根神经上评估了该算法对有经验和无经验用户以及盲目搜索算法（BFA）的性能。然后，我们在六根受远端感觉丧失影响的神经病变神经上验证了基于 AI 的平台。
我们的自动化方法能够找到神经刺激的最佳值，从而产生可靠且舒适的诱发感觉。与替代方法相比，RL 优于无经验和 BFA，显著减少了映射时间和刺激训练次数，同时提高了整体质量。此外，RL 算法的性能与经过训练的实验者相当。最后，我们成功地将其用于诱发神经病变患者的感觉反馈。
我们的研究结果表明，基于 RL 算法的 AI 平台可以自动且有效地校准体感神经刺激的参数。这有望避免在类似情况下使用专家，得益于人工智能和神经技术的融合。我们的 RL 算法具有在需要刺激参数映射过程的其他神经调节领域中使用的潜力。
ClinicalTrials.gov（标识符：NCT04217005）。
