An automated magnetoencephalographic data cleaning algorithm. Issue 14 (26th October 2019)

• Record Type: Journal Article
• Authors: Sorriso, Antonietta; Sorrentino, Pierpaolo; Rucco, Rosaria; Mandolesi, Laura; Ferraioli, Giampaolo; Franceschini, Stefano; Ambrosanio, Michele; Baselice, Fabio
• Is Part Of: Computer methods in biomechanics and biomedical engineering. Volume 22:Issue 14(2019)
• Page Start: 1116
• Page End: 1125
• Publication Date: 2019-10-26
• Subjects: Magnetoencephalography (MEG) -- bad data -- noise reduction -- automated artifact removal; Biomechanics -- Data processing -- Periodicals; Biomedical engineering -- Periodicals; Biomechanics -- Periodicals; Biomedical Engineering -- methods -- Periodicals; Computing Methodologies -- Periodicals; 612.7
• DOI: 10.1080/10255842.2019.1634695
• Languages: English
• ISSNs: 1025-5842

Abstract

Abstract: The problem of cleaning magnetoencephalographic data is addressed in this manuscript. At present, several denoising procedures have been proposed in the literature, nevertheless their adoption is limited due to the difficulty in implementing and properly tuning the algorithms. Therefore, as of today, the gold standard remains manual cleaning. We propose an approach developed with the aim of automating each step of the manual cleaning. Its peculiarities are the ease of implementation and using and the remarkable reproducibility of the results. Interestingly, the algorithm has been designed to imitate the reasoning behind the manual procedure, carried out by trained experts. Our statistical analysis shows that no significant differences can be found between the two approaches.