SANTIA: a Matlab-based open-source toolbox for artifact detection and removal from extracellular neuronal signals

Brain Informatics

Ahmad Lotfi¹  Marcos Fabietti¹  Mufti Mahmud²  Alberto Averna³  David J. Guggenmos⁴  Randolph J. Nudo⁴  Michela Chiappalone⁵  Jianhui Chen⁶  M. Shamim Kaiser⁷ 

[1] Department of Computer Science, Nottingham Trent University, Clifton Lane, NG11 8NS, Nottingham, UK;Department of Computer Science, Nottingham Trent University, Clifton Lane, NG11 8NS, Nottingham, UK;Medical Technologies Innovation Facility, Nottingham Trent University, Clifton Lane, NG11 8NS, Nottingham, UK;Computing and Informatics Research Centre, Nottingham Trent University, Clifton Lane, NG11 8NS, Nottingham, UK;Department of Health Sciences, University of Milan, Via di Rudinì, 8, 20142, Milan, Italy;Department of Physical Medicine and Rehabilitation, University of Kansas Medical Center, 3901 Rainbow Blvd, 66160, Kansas City, USA;Department of informatics, Bioengineering, Robotics and System Engineering-DIBRIS, University of Genova, Via All’Opera Pia, 13, 16145, Genoa, Italy;Faculty of Information Technology, International WIC Institute, Beijing University of Technology, 100124, Beijing, China;Beijing International Collaboration Base on Brain Informatics and Wisdom Services, 100124, Beijing, China;Institute of Information Technology, Jahangirnagar University, Savar, 1342, Dhaka, Bangladesh;

关键词: Local field potential;    Artifacts;    Neural networks;    Machine learning;    Neuronal signals;   

DOI  :  10.1186/s40708-021-00135-3

来源: Springer

【 摘 要 】

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.

【 授权许可】

CC BY