Reynaud Sarah, Merlini Adrien, Ben Salem Douraied, Rousseau François
IMT Atlantique, LaTIM U1101 INSERM, Brest, France.
IMT Atlantique, Lab-STICC UMR CNRS 6285, Brest, France.
Front Neurosci. 2024 Nov 27;18:1444935. doi: 10.3389/fnins.2024.1444935. eCollection 2024.
Electroencephalography source imaging (ESI) is an ill-posed inverse problem: an additional constraint is needed to find a unique solution. The choice of this constraint, or prior, remains a challenge for most ESI methods. This work explores the application of supervised learning methods for spatio-temporal ESI, where the relationship between measurements and sources is learned directly from the data. Three neural networks were trained on synthetic data and compared with non-learning based methods. Two distinct types of simulation, each based on different models of brain electrical activity, were employed to quantitatively assess the generalization capabilities of the neural networks and the impact of training data on their performances, using five complementary metrics. The results demonstrate that, with appropriately designed simulations, neural networks can be competitive with non-learning-based approaches, even when applied to previously unseen data.
脑电图源成像（ESI）是一个不适定的逆问题：需要额外的约束来找到唯一解。对于大多数ESI方法而言，这种约束或先验的选择仍然是一个挑战。这项工作探索了监督学习方法在时空ESI中的应用，其中测量值与源之间的关系是直接从数据中学习得到的。在合成数据上训练了三个神经网络，并与基于非学习的方法进行了比较。采用了两种不同类型的模拟，每种模拟基于不同的脑电活动模型，使用五个互补指标来定量评估神经网络的泛化能力以及训练数据对其性能的影响。结果表明，通过适当设计模拟，即使应用于以前未见过的数据，神经网络也可以与基于非学习的方法相竞争。
