运动诱发电位募集曲线的分层贝叶斯估计可产生准确且鲁棒的估计结果
Hierarchical Bayesian estimation of motor-evoked potential recruitment curves yields accurate and robust estimates
AI总结：
针对现有运动诱发电位募集曲线参数估计需大样本的问题，提出带整流逻辑函数的分层贝叶斯框架，在小样本下精度更优，还提供了Python开源库hbMEP。
摘要：
电磁刺激可探测并调节控制运动的神经系统。理解其作用的关键是肌肉募集曲线，该曲线将诱发电位幅值与刺激强度对应起来。当前估计曲线参数的方法需要大量样本；但受实验条件限制，获取大量样本通常并不现实。本文提出一种分层贝叶斯框架，可适配小样本、处理异常值、生成高保真模拟数据，并返回曲线参数的后验分布以量化估计不确定性。该框架采用整流逻辑函数估计运动阈值，交叉验证结果显示其预测性能优于传统使用的S型替代函数。仿真实验中，该方法在稀疏数据下降低了阈值估计误差，表现优于非分层模型；与频率论检验相比，它只需更少的受试者即可检测到阈值变化。本文展示了电刺激和电磁刺激数据两个常见用例，并提供了名为hbMEP的Python开源库，可用于多种应用场景。
英文摘要：
Electromagnetic stimulation probes and modulates the neural systems that control movement. Key to understanding their effects is the muscle recruitment curve, which maps evoked potential size against stimulation intensity. Current methods to estimate curve parameters require large samples; however, obtaining these is often impractical due to experimental constraints. Here, we present a hierarchical Bayesian framework that accounts for small samples, handles outliers, simulates high-fidelity data, and returns a posterior distribution over curve parameters that quantify estimation uncertainty. It uses a rectified-logistic function that estimates motor threshold and outperforms conventionally used sigmoidal alternatives in predictive performance, as demonstrated through cross-validation. In simulations, our method outperforms non-hierarchical models by reducing threshold estimation error on sparse data and requires fewer participants to detect shifts in threshold compared to frequentist testing. We present two common use cases involving electrical and electromagnetic stimulation data and provide an open-source library for Python, called hbMEP, for diverse applications.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
