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.
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。