Abstract：
BACKGROUND: Transcranial magnetic stimulation (TMS) is a valuable technique for assessing the function of the motor cortex and cortico-muscular pathways. TMS activates the motoneurons in the cortex, which after transmission along cortico-muscular pathways can be measured as motor-evoked potentials (MEPs). The position and orientation of the TMS coil and the intensity used to deliver a TMS pulse are considered central TMS setup parameters influencing the presence/absence of MEPs.
METHODS: We sought to predict the presence of MEPs from TMS setup parameters using machine learning. We trained different machine learners using either within-subject or between-subject designs.
RESULTS: We obtained prediction accuracies of on average 77 % and 65 % with maxima up to up to 90 % and 72 % within and between subjects, respectively. Across the board, a bagging ensemble appeared to be the most suitable approach to predict the presence of MEPs.
CONCLUSIONS: Although within a subject the prediction of MEPs via TMS setup parameter-based machine learning might be feasible, the limited accuracy between subjects suggests that the transfer of this approach to experimental or clinical research comes with significant challenges.
METHODS: We sought to predict the presence of MEPs from TMS setup parameters using machine learning. We trained different machine learners using either within-subject or between-subject designs.
RESULTS: We obtained prediction accuracies of on average 77 % and 65 % with maxima up to up to 90 % and 72 % within and between subjects, respectively. Across the board, a bagging ensemble appeared to be the most suitable approach to predict the presence of MEPs.
CONCLUSIONS: Although within a subject the prediction of MEPs via TMS setup parameter-based machine learning might be feasible, the limited accuracy between subjects suggests that the transfer of this approach to experimental or clinical research comes with significant challenges.
摘要：
背景：经颅磁刺激（TMS）是一种评估运动皮质和皮质-肌肉通路功能的有价值的技术。TMS激活皮层中的运动神经元，在沿着皮质-肌肉途径传播后，可以测量为运动诱发电位（MEP）。TMS线圈的位置和取向以及用于递送TMS脉冲的强度被认为是影响MEP的存在/不存在的中心TMS设置参数。
方法：我们试图使用机器学习从TMS设置参数预测MEP的存在。我们使用学科内或学科间的设计来训练不同的机器学习者。
结果：我们获得了平均77％和65％的预测精度，在受试者内部和受试者之间的最大值高达90％和72％，分别。全盘,套袋集合似乎是预测MEP存在的最合适方法。
结论：尽管在受试者中，通过基于TMS设置参数的机器学习来预测MEP可能是可行的，受试者之间的准确性有限，这表明将这种方法转移到实验或临床研究中带来了巨大的挑战。
方法：我们试图使用机器学习从TMS设置参数预测MEP的存在。我们使用学科内或学科间的设计来训练不同的机器学习者。
结果：我们获得了平均77％和65％的预测精度，在受试者内部和受试者之间的最大值高达90％和72％，分别。全盘,套袋集合似乎是预测MEP存在的最合适方法。
结论：尽管在受试者中，通过基于TMS设置参数的机器学习来预测MEP可能是可行的，受试者之间的准确性有限，这表明将这种方法转移到实验或临床研究中带来了巨大的挑战。
