Fabietti Marcos, Mahmud Mufti, Lotfi Ahmad, Kaiser M Shamim, Averna Alberto, Guggenmos David J, Nudo Randolph J, Chiappalone Michela, Chen Jianhui
Department of Computer Science, Nottingham Trent University, Clifton Lane, Nottingham, NG11 8NS, UK.
Medical Technologies Innovation Facility, Nottingham Trent University, Clifton Lane, Nottingham, NG11 8NS, UK.
Brain Inform. 2021 Jul 20;8(1):14. doi: 10.1186/s40708-021-00135-3.
Neuronal signals generally represent activation of the neuronal networks and give insights into brain functionalities. They are considered as fingerprints of actions and their processing across different structures of the brain. These recordings generate a large volume of data that are susceptible to noise and artifacts. Therefore, the review of these data to ensure high quality by automatically detecting and removing the artifacts is imperative. Toward this aim, this work proposes a custom-developed automatic artifact removal toolbox named, SANTIA (SigMate Advanced: a Novel Tool for Identification of Artifacts in Neuronal Signals). Developed in Matlab, SANTIA is an open-source toolbox that applies neural network-based machine learning techniques to label and train models to detect artifacts from the invasive neuronal signals known as local field potentials.
神经元信号通常代表神经网络的激活,并能深入了解大脑功能。它们被视为大脑不同结构中行为及其处理过程的指纹。这些记录会产生大量易受噪声和伪迹影响的数据。因此,通过自动检测和去除伪迹来审查这些数据以确保高质量是必不可少的。为了实现这一目标,这项工作提出了一个定制开发的自动伪迹去除工具箱,名为SANTIA(SigMate Advanced:一种用于识别神经元信号中伪迹的新型工具)。SANTIA是在Matlab中开发的开源工具箱,它应用基于神经网络的机器学习技术来标记和训练模型,以从称为局部场电位的侵入性神经元信号中检测伪迹。