Repairbads: An automatic and adaptive method to repair bad channels and segments for OPM-MEG

Fulong Wang; Yujie Ma; Tianyu Gao; Yue Tao; Ruonan Wang; Ruochen Zhao; Fuzhi Cao; Yang Gao; Xiaolin Ning

doi:10.1016/j.neuroimage.2024.120996

Repairbads: An automatic and adaptive method to repair bad channels and segments for OPM-MEG

Neuroimage. 2025 Jan 6:120996. doi: 10.1016/j.neuroimage.2024.120996. Online ahead of print.

Authors

Fulong Wang¹, Yujie Ma², Tianyu Gao³, Yue Tao⁴, Ruonan Wang⁵, Ruochen Zhao⁶, Fuzhi Cao⁷, Yang Gao⁸, Xiaolin Ning⁹

Affiliations

¹ School of Instrumentation Science and Optoelectronic Engineering, Beihang University, Beijing, 100191, China; Key Laboratory of Ultra-Weak Magnetic Field Measurement Technology, Ministry of Education, School of Instrumentation and Optoelectronic Engineering, Beihang University, 100191, Beijing, China; Institute of Large-scale Scientific Facility and Centre for Zero Magnetic Field Science, Beihang University, Hangzhou, 310051, China. Electronic address: [email protected].
² School of Instrumentation Science and Optoelectronic Engineering, Beihang University, Beijing, 100191, China; Key Laboratory of Ultra-Weak Magnetic Field Measurement Technology, Ministry of Education, School of Instrumentation and Optoelectronic Engineering, Beihang University, 100191, Beijing, China; Institute of Large-scale Scientific Facility and Centre for Zero Magnetic Field Science, Beihang University, Hangzhou, 310051, China. Electronic address: [email protected].
³ School of Instrumentation Science and Optoelectronic Engineering, Beihang University, Beijing, 100191, China; Key Laboratory of Ultra-Weak Magnetic Field Measurement Technology, Ministry of Education, School of Instrumentation and Optoelectronic Engineering, Beihang University, 100191, Beijing, China; Institute of Large-scale Scientific Facility and Centre for Zero Magnetic Field Science, Beihang University, Hangzhou, 310051, China. Electronic address: [email protected].
⁴ School of Instrumentation Science and Optoelectronic Engineering, Beihang University, Beijing, 100191, China; Key Laboratory of Ultra-Weak Magnetic Field Measurement Technology, Ministry of Education, School of Instrumentation and Optoelectronic Engineering, Beihang University, 100191, Beijing, China; Institute of Large-scale Scientific Facility and Centre for Zero Magnetic Field Science, Beihang University, Hangzhou, 310051, China. Electronic address: [email protected].
⁵ School of Instrumentation Science and Optoelectronic Engineering, Beihang University, Beijing, 100191, China; Key Laboratory of Ultra-Weak Magnetic Field Measurement Technology, Ministry of Education, School of Instrumentation and Optoelectronic Engineering, Beihang University, 100191, Beijing, China; Institute of Large-scale Scientific Facility and Centre for Zero Magnetic Field Science, Beihang University, Hangzhou, 310051, China. Electronic address: [email protected].
⁶ School of Instrumentation Science and Optoelectronic Engineering, Beihang University, Beijing, 100191, China; Key Laboratory of Ultra-Weak Magnetic Field Measurement Technology, Ministry of Education, School of Instrumentation and Optoelectronic Engineering, Beihang University, 100191, Beijing, China; Institute of Large-scale Scientific Facility and Centre for Zero Magnetic Field Science, Beihang University, Hangzhou, 310051, China. Electronic address: [email protected].
⁷ School of Instrumentation Science and Optoelectronic Engineering, Beihang University, Beijing, 100191, China; Key Laboratory of Ultra-Weak Magnetic Field Measurement Technology, Ministry of Education, School of Instrumentation and Optoelectronic Engineering, Beihang University, 100191, Beijing, China; Institute of Large-scale Scientific Facility and Centre for Zero Magnetic Field Science, Beihang University, Hangzhou, 310051, China. Electronic address: [email protected].
⁸ School of Instrumentation Science and Optoelectronic Engineering, Beihang University, Beijing, 100191, China; Key Laboratory of Ultra-Weak Magnetic Field Measurement Technology, Ministry of Education, School of Instrumentation and Optoelectronic Engineering, Beihang University, 100191, Beijing, China; Institute of Large-scale Scientific Facility and Centre for Zero Magnetic Field Science, Beihang University, Hangzhou, 310051, China. Electronic address: [email protected].
⁹ School of Instrumentation Science and Optoelectronic Engineering, Beihang University, Beijing, 100191, China; Key Laboratory of Ultra-Weak Magnetic Field Measurement Technology, Ministry of Education, School of Instrumentation and Optoelectronic Engineering, Beihang University, 100191, Beijing, China; National Innovation Platform for industry-Education Integration in Medicine-Engineering Interdisciplinary, Shandong Key Laboratory for Magnetic Field-free Medicine and Functional Imaging, Shandong University, Research Institute of Shandong University, Jinan, 250014, China; National Institute of Extremely-Weak Magnetic Field Infrastructure, Hangzhou, 310051, China; State Key Laboratory of Traditional Chinese Medicine Syndrome/Health Construction Center, The Second Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou, 510120, China; Hefei National Laboratory, Hefei, 230088, China. Electronic address: [email protected].

PMID: 39778818
DOI: 10.1016/j.neuroimage.2024.120996

Abstract

The optically pumped magnetometer (OPM) based magnetoencephalography (MEG) system offers advantages such as flexible layout and wearability. However, the position instability or jitter of OPM sensors can result in bad channels and segments, which significantly impede subsequent preprocessing and analysis. Most common methods directly reject or interpolate to repair these bad channels and segments. Direct rejection leads to data loss, and when the number of sensors is limited, interpolation using neighboring sensors can cause significant signal distortion and cannot repair bad segments present in all channels. Therefore, most existing methods are unsuitable for OPM-MEG systems with fewer channels. We introduce an automatic bad segments and bad channels repair method for OPM-MEG, called Repairbads. This method aims to repair all bad data and reduce signal distortion, especially capable of automatically repairing bad segments present in all channels simultaneously. Repairbads employs Riemannian Potato combined with joint decorrelation to project out artifact components, achieving automatic bad segment repair. Then, an adaptive algorithm is used to segment the signal into relatively stable noise data chunks, and the source-estimate-utilizing noise-discarding algorithm is applied to each chunk to achieve automatic bad channel repair. We compared the performance of Repairbads with the Autoreject method on both simulated and real auditory evoked data, using five evaluation metrics for quantitative assessment. The results demonstrate that Repairbads consistently outperforms across all five metrics. In both simulated and real OPM-MEG data, Repairbads shows better performance than current state-of-the-art methods, reliably repairing bad data with minimal distortion. The automation of this method significantly reduces the burden of manual inspection, promoting the automated processing and clinical application of OPM-MEG.

Keywords: Automatic repair; Bad segments and bad channels; Magnetoencephalography (MEG); Optically Pumped Magnetometers (OPM); Preprocessing.