Reconstruction of Low-Quality Channel Data in Magnetoencephalography using Surface Reconstruction and Interpolation Methods
-
Hanie Arabian
,
Alireza Karimian
,
Hamid Reza Marateb
,
Carolina Migliorelli
,
Miquel Angel Mañanas
,
Sergio Romero
,
Antonio Russi
,
Rafał Nowak
Abstract
Purpose: Magnetoencephalography (MEG) is a brain imaging method with high temporal and acceptable spatial resolution, achieved by recording neural magnetic fields. The data quality of this imaging method is compromised due to reasons such as the failure of one or more sensors. This study aims to explore the efficiency of the various data reconstruction techniques in MEG for recovering poor-quality or missing channel data.
Materials and Methods: We compared three surface reconstruction methods (Mean, Median, and Trimmed mean), two partial differential equations (modified Poisson and Diffusion equation), and a Finite Element-based interpolation method using data from 11 young adults (aged 30±12). Using varying levels of simulated data loss (2%, 5%, 11%, and 16%), we assessed each method in terms of time taken for reconstruction, R-squared, root mean squared error (RMSE), and signal-to-noise ratio (SNR) compared to a reference signal. Statistical tests (P-value < 0.05) were used to analyze the relationships between the mentioned evaluation criteria. Generalized Linear Models revealed that surface reconstruction methods and finite-element interpolation outperformed partial differential equations.
Results: The Trimmed mean method achieved the highest R-squared (0.882 ± 0.0610) and lowest RMSE (0.0155 ± 0.00904) with a reconstruction time of 9.5154 microseconds for a 500-millisecond epoch of MEG channel data.
Conclusion: The surface reconstruction methods can recover the noisy or lost signal in MEG with a suitable error and required time. These findings support the use of robust statistical strategies for improving MEG signal quality, especially in high-density sensor arrays.
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7- Mithun Diwakar et al., "Accurate reconstruction of temporal correlation for neuronal sources using the enhanced dual-core MEG beamformer." NeuroImage, Vol. 56 (No. 4), pp. 1918-28, (2011).
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11- Vinh Tan et al., "Preliminary Observations of Resting-State Magnetoencephalography in Nonmedicated Children with Obsessive-Compulsive Disorder." Journal of Child and Adolescent Psychopharmacology, Vol. 32 (No. 10), pp. 522-32, (2022).
12- Bhargava K. Gautham et al., "Automated lateralization of temporal lobe epilepsy with cross frequency coupling using magnetoencephalography." Biomedical Signal Processing and Control, Vol. 72p. 103294, 2022/02/01/ (2022).
13- Víctor Rodríguez-González et al., "Unveiling the alterations in the frequency-dependent connectivity structure of MEG signals in mild cognitive impairment and Alzheimer’s disease." Biomedical Signal Processing and Control, Vol. 87p. 105512, 2024/01/01/ (2024).
14- Pinar Ozel, Ali Karaca, Ali Olamat, Aydin Akan, Mehmet Akif Ozcoban, and Oguz Tan, "Intrinsic synchronization analysis of brain activity in obsessive–compulsive disorders." International Journal of Neural Systems, Vol. 30 (No. 09), p. 2050046, (2020).
15- Dengxuan Bai, Wenpo Yao, Shuwang Wang, Wei Yan, and Jun Wang, "Recurrence network analysis of schizophrenia MEG under different stimulation states." Biomedical Signal Processing and Control, Vol. 80p. 104310, 2023/02/01/ (2023).
16- Jan‐Mathijs Schoffelen and Joachim Gross, "Source connectivity analysis with MEG and EEG." Human Brain Mapping, Vol. 30 (No. 6), pp. 1857-65, (2009).
17- Zhenfeng Gao, Fuzhi Cao, Nan An, and Xiaolin Ning, "Automatic co-registration of OPM-MEG and MRI using a 3D laser scanner." Measurement, Vol. 223p. 113729, 2023/12/01/ (2023).
18- Alain de Cheveigné and Dorothée Arzounian, "Robust detrending, rereferencing, outlier detection, and inpainting for multichannel data." NeuroImage, Vol. 172pp. 903-12, (2018).
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20- Joachim Gross et al., "Good practice for conducting and reporting MEG research." NeuroImage, Vol. 65pp. 349-63, (2013).
21- Ruonan Wang, Zhihui Jia, Ruochen Zhao, Yang Gao, and Xiaolin Ning, "OPM-MEG bad channel identification method based on the improved box-isolation forest algorithm." Measurement, Vol. 224p. 113948, 2024/01/01/ (2024).
22- Mikko A Uusitalo and Risto J Ilmoniemi, "Signal-space projection method for separating MEG or EEG into components." Medical and biological engineering and computing, Vol. 35 (No. 2), pp. 135-40, (1997).
23- Andrea Mognon, Jorge Jovicich, Lorenzo Bruzzone, and Marco Buiatti, "ADJUST: An automatic EEG artifact detector based on the joint use of spatial and temporal features." Psychophysiology, Vol. 48 (No. 2), pp. 229-40, (2011).
24- Michel Besserve, Karim Jerbi, Francois Laurent, Sylvain Baillet, Jacques Martinerie, and Line Garnero, "Classification methods for ongoing EEG and MEG signals." Biological research, Vol. 40 (No. 4), pp. 415-37, (2007).
25- Keita Suzuki and Okito Yamashita, "MEG current source reconstruction using a meta-analysis fMRI prior." NeuroImage, Vol. 236p. 118034, (2021).
26- K. Sekihara, K. E. Hild, 2nd, and S. S. Nagarajan, "A novel adaptive beamformer for MEG source reconstruction effective when large background brain activities exist." IEEE Trans Biomed Eng, Vol. 53 (No. 9), pp. 1755-64, Sep (2006).
27- Matthew J. Brookes et al., "Beamformer reconstruction of correlated sources using a modified source model." NeuroImage, Vol. 34 (No. 4), pp. 1454-65, (2007).
28- Alain de Cheveigné and Jonathan Simon, "Sensor Noise Suppression." Journal of Neuroscience Methods, Vol. 168pp. 195-202, (2008).
29- Makoto Fukushima, Okito Yamashita, Thomas R. Knösche, and Masa-aki Sato, "MEG source reconstruction based on identification of directed source interactions on whole-brain anatomical networks." NeuroImage, Vol. 105pp. 408-27, (2015).
30- Alain de Cheveigné, "Sparse time artifact removal." Journal of Neuroscience Methods, Vol. 262pp. 14-20, (2016).
31- Hanie Arabian, Alireza Karimian, and Hamid R Marateb, "Repair of Poor Signal of Magnetoencephalography Channel Data." Frontiers in Biomedical Technologies, Vol. 0 (No. 0), 05/10 (2024).
32- Carolina Migliorelli, Joan F Alonso, Sergio Romero, Rafał Nowak, Antonio Russi, and Miguel A Mañanas, "Automated detection of epileptic ripples in MEG using beamformer-based virtual sensors." Journal of Neural Engineering, Vol. 14 (No. 4), p. 046013, (2017).
33- F. Tadel, S. Baillet, J. C. Mosher, D. Pantazis, and R. M. Leahy, "Brainstorm: a user-friendly application for MEG/EEG analysis." (in eng), Comput Intell Neurosci, Vol. 2011p. 879716, (2011).
34- Robert Oostenveld, Pascal Fries, Eric Maris, and Jan-Mathijs Schoffelen, "FieldTrip: Open Source Software for Advanced Analysis of MEG, EEG, and Invasive Electrophysiological Data." Vol. 2011p. 156869, (2011).
35- Simon Little, James Bonaiuto, Sofie S. Meyer, Jose Lopez, Sven Bestmann, and Gareth Barnes, "Quantifying the performance of MEG source reconstruction using resting state data." NeuroImage, Vol. 181pp. 453-60, (2018).
36- P. Ghaderi and H. R. Marateb, "Muscle Activity Map Reconstruction from High Density Surface EMG Signals With Missing Channels Using Image Inpainting and Surface Reconstruction Methods." IEEE Transactions on Biomedical Engineering, Vol. 64 (No. 7), pp. 1513-23, (2017).
37- Hamid R Marateb, Monica Rojas-Martinez, Marjan Mansourian, Roberto Merletti, and Miguel A Mananas Villanueva, "Outlier detection in high-density surface electromyographic signals." Medical & biological engineering & computing, Vol. 50pp. 79-89, (2012).
38- M. S. Hämäläinen and R. J. Ilmoniemi, "Interpreting magnetic fields of the brain: minimum norm estimates." (in eng), Med Biol Eng Comput, Vol. 32 (No. 1), pp. 35-42, Jan (1994).
39- S. Taulu, J. Simola, and M. Kajola, "Applications of the signal space separation method." IEEE Transactions on Signal Processing, Vol. 53 (No. 9), pp. 3359-72, (2005).
40- Jiwoon Lee and Cheolsoo Park, "Restoration of Time-Series Medical Data with Diffusion Model." in 2022 IEEE International Conference on Consumer Electronics-Asia (ICCE-Asia), (2022): IEEE, pp. 1-4.
41- A. J. Dobson, & Barnett, A., An Introduction to Generalized Linear Models. Fourth Edition ed. CRC Press, (2018).
42- Tuomas Vainio, Teemu Mäkelä, Sauli Savolainen, and Marko Kangasniemi, "Performance of a 3D convolutional neural network in the detection of hypoperfusion at CT pulmonary angiography in patients with chronic pulmonary embolism: a feasibility study." European radiology experimental, Vol. 5 (No. 1), pp. 1-12, (2021).
43- R Core Team. (2022). R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. [Online]. Available: https://www.R-project.org/.
44- I. C Marschner, " glm2: Fitting generalized linear models with convergence problems." The R Journal, Vol. 3(2), pp. 12-15, (2011).
2- Luke Tait, Ayşegül Özkan, Maciej J Szul, and Jiaxiang Zhang, "A systematic evaluation of source reconstruction of resting MEG of the human brain with a new high‐resolution atlas: Performance, precision, and parcellation." Human Brain Mapping, Vol. 42 (No. 14), pp. 4685-707, (2021).
3- J. M. Lina, R. Chowdhury, E. Lemay, E. Kobayashi, and C. Grova, "Wavelet-Based Localization of Oscillatory Sources From Magnetoencephalography Data." IEEE Transactions on Biomedical Engineering, Vol. 61 (No. 8), pp. 2350-64, (2014).
4- Matthew J. Brookes, Mark W. Woolrich, and Darren Price, "An Introduction to MEG Connectivity Measurements." in Magnetoencephalography: From Signals to Dynamic Cortical Networks, Selma Supek and Cheryl J. Aine, Eds. Cham: Springer International Publishing, (2019), pp. 433-70.
5- J. Vrba and S. E. Robinson, "SQUID sensor array configurations for magnetoencephalography applications." Superconductor Science and Technology, Vol. 15 (No. 9), pp. R51-R89, (2002).
6- Masashi Sato, Okito Yamashita, Masa-aki Sato, and Yoichi Miyawaki, "Information spreading by a combination of MEG source estimation and multivariate pattern classification." PloS one, Vol. 13 (No. 6), p. e0198806, (2018).
7- Mithun Diwakar et al., "Accurate reconstruction of temporal correlation for neuronal sources using the enhanced dual-core MEG beamformer." NeuroImage, Vol. 56 (No. 4), pp. 1918-28, (2011).
8- Adam P. Baker et al., "Fast transient networks in spontaneous human brain activity." Elife, Vol. 3p. e01867, 2014/03/25 (2014).
9- Monica Garcia-Alloza and Brian J. Bacskai, "Techniques for brain imaging in vivo." NeuroMolecular Medicine, Vol. 6 (No. 1), pp. 65-78, 2004/08/01 (2004).
10- Matti S. Hämäläinen, "Magnetoencephalography: A tool for functional brain imaging." Brain Topography, Vol. 5 (No. 2), pp. 95-102, (1992).
11- Vinh Tan et al., "Preliminary Observations of Resting-State Magnetoencephalography in Nonmedicated Children with Obsessive-Compulsive Disorder." Journal of Child and Adolescent Psychopharmacology, Vol. 32 (No. 10), pp. 522-32, (2022).
12- Bhargava K. Gautham et al., "Automated lateralization of temporal lobe epilepsy with cross frequency coupling using magnetoencephalography." Biomedical Signal Processing and Control, Vol. 72p. 103294, 2022/02/01/ (2022).
13- Víctor Rodríguez-González et al., "Unveiling the alterations in the frequency-dependent connectivity structure of MEG signals in mild cognitive impairment and Alzheimer’s disease." Biomedical Signal Processing and Control, Vol. 87p. 105512, 2024/01/01/ (2024).
14- Pinar Ozel, Ali Karaca, Ali Olamat, Aydin Akan, Mehmet Akif Ozcoban, and Oguz Tan, "Intrinsic synchronization analysis of brain activity in obsessive–compulsive disorders." International Journal of Neural Systems, Vol. 30 (No. 09), p. 2050046, (2020).
15- Dengxuan Bai, Wenpo Yao, Shuwang Wang, Wei Yan, and Jun Wang, "Recurrence network analysis of schizophrenia MEG under different stimulation states." Biomedical Signal Processing and Control, Vol. 80p. 104310, 2023/02/01/ (2023).
16- Jan‐Mathijs Schoffelen and Joachim Gross, "Source connectivity analysis with MEG and EEG." Human Brain Mapping, Vol. 30 (No. 6), pp. 1857-65, (2009).
17- Zhenfeng Gao, Fuzhi Cao, Nan An, and Xiaolin Ning, "Automatic co-registration of OPM-MEG and MRI using a 3D laser scanner." Measurement, Vol. 223p. 113729, 2023/12/01/ (2023).
18- Alain de Cheveigné and Dorothée Arzounian, "Robust detrending, rereferencing, outlier detection, and inpainting for multichannel data." NeuroImage, Vol. 172pp. 903-12, (2018).
19- John G Samuelsson, Noam Peled, Fahimeh Mamashli, Jyrki Ahveninen, and Matti S Hämäläinen, "Spatial fidelity of MEG/EEG source estimates: A general evaluation approach." NeuroImage, Vol. 224p. 117430, (2021).
20- Joachim Gross et al., "Good practice for conducting and reporting MEG research." NeuroImage, Vol. 65pp. 349-63, (2013).
21- Ruonan Wang, Zhihui Jia, Ruochen Zhao, Yang Gao, and Xiaolin Ning, "OPM-MEG bad channel identification method based on the improved box-isolation forest algorithm." Measurement, Vol. 224p. 113948, 2024/01/01/ (2024).
22- Mikko A Uusitalo and Risto J Ilmoniemi, "Signal-space projection method for separating MEG or EEG into components." Medical and biological engineering and computing, Vol. 35 (No. 2), pp. 135-40, (1997).
23- Andrea Mognon, Jorge Jovicich, Lorenzo Bruzzone, and Marco Buiatti, "ADJUST: An automatic EEG artifact detector based on the joint use of spatial and temporal features." Psychophysiology, Vol. 48 (No. 2), pp. 229-40, (2011).
24- Michel Besserve, Karim Jerbi, Francois Laurent, Sylvain Baillet, Jacques Martinerie, and Line Garnero, "Classification methods for ongoing EEG and MEG signals." Biological research, Vol. 40 (No. 4), pp. 415-37, (2007).
25- Keita Suzuki and Okito Yamashita, "MEG current source reconstruction using a meta-analysis fMRI prior." NeuroImage, Vol. 236p. 118034, (2021).
26- K. Sekihara, K. E. Hild, 2nd, and S. S. Nagarajan, "A novel adaptive beamformer for MEG source reconstruction effective when large background brain activities exist." IEEE Trans Biomed Eng, Vol. 53 (No. 9), pp. 1755-64, Sep (2006).
27- Matthew J. Brookes et al., "Beamformer reconstruction of correlated sources using a modified source model." NeuroImage, Vol. 34 (No. 4), pp. 1454-65, (2007).
28- Alain de Cheveigné and Jonathan Simon, "Sensor Noise Suppression." Journal of Neuroscience Methods, Vol. 168pp. 195-202, (2008).
29- Makoto Fukushima, Okito Yamashita, Thomas R. Knösche, and Masa-aki Sato, "MEG source reconstruction based on identification of directed source interactions on whole-brain anatomical networks." NeuroImage, Vol. 105pp. 408-27, (2015).
30- Alain de Cheveigné, "Sparse time artifact removal." Journal of Neuroscience Methods, Vol. 262pp. 14-20, (2016).
31- Hanie Arabian, Alireza Karimian, and Hamid R Marateb, "Repair of Poor Signal of Magnetoencephalography Channel Data." Frontiers in Biomedical Technologies, Vol. 0 (No. 0), 05/10 (2024).
32- Carolina Migliorelli, Joan F Alonso, Sergio Romero, Rafał Nowak, Antonio Russi, and Miguel A Mañanas, "Automated detection of epileptic ripples in MEG using beamformer-based virtual sensors." Journal of Neural Engineering, Vol. 14 (No. 4), p. 046013, (2017).
33- F. Tadel, S. Baillet, J. C. Mosher, D. Pantazis, and R. M. Leahy, "Brainstorm: a user-friendly application for MEG/EEG analysis." (in eng), Comput Intell Neurosci, Vol. 2011p. 879716, (2011).
34- Robert Oostenveld, Pascal Fries, Eric Maris, and Jan-Mathijs Schoffelen, "FieldTrip: Open Source Software for Advanced Analysis of MEG, EEG, and Invasive Electrophysiological Data." Vol. 2011p. 156869, (2011).
35- Simon Little, James Bonaiuto, Sofie S. Meyer, Jose Lopez, Sven Bestmann, and Gareth Barnes, "Quantifying the performance of MEG source reconstruction using resting state data." NeuroImage, Vol. 181pp. 453-60, (2018).
36- P. Ghaderi and H. R. Marateb, "Muscle Activity Map Reconstruction from High Density Surface EMG Signals With Missing Channels Using Image Inpainting and Surface Reconstruction Methods." IEEE Transactions on Biomedical Engineering, Vol. 64 (No. 7), pp. 1513-23, (2017).
37- Hamid R Marateb, Monica Rojas-Martinez, Marjan Mansourian, Roberto Merletti, and Miguel A Mananas Villanueva, "Outlier detection in high-density surface electromyographic signals." Medical & biological engineering & computing, Vol. 50pp. 79-89, (2012).
38- M. S. Hämäläinen and R. J. Ilmoniemi, "Interpreting magnetic fields of the brain: minimum norm estimates." (in eng), Med Biol Eng Comput, Vol. 32 (No. 1), pp. 35-42, Jan (1994).
39- S. Taulu, J. Simola, and M. Kajola, "Applications of the signal space separation method." IEEE Transactions on Signal Processing, Vol. 53 (No. 9), pp. 3359-72, (2005).
40- Jiwoon Lee and Cheolsoo Park, "Restoration of Time-Series Medical Data with Diffusion Model." in 2022 IEEE International Conference on Consumer Electronics-Asia (ICCE-Asia), (2022): IEEE, pp. 1-4.
41- A. J. Dobson, & Barnett, A., An Introduction to Generalized Linear Models. Fourth Edition ed. CRC Press, (2018).
42- Tuomas Vainio, Teemu Mäkelä, Sauli Savolainen, and Marko Kangasniemi, "Performance of a 3D convolutional neural network in the detection of hypoperfusion at CT pulmonary angiography in patients with chronic pulmonary embolism: a feasibility study." European radiology experimental, Vol. 5 (No. 1), pp. 1-12, (2021).
43- R Core Team. (2022). R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. [Online]. Available: https://www.R-project.org/.
44- I. C Marschner, " glm2: Fitting generalized linear models with convergence problems." The R Journal, Vol. 3(2), pp. 12-15, (2011).
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| Issue | Vol 13 No 3 (2026) | |
| Section | Original Article(s) | |
| DOI | https://doi.org/10.18502/fbt.v13i3.22733 | |
| Keywords | ||
| Data reconstruction Finite element interpolation Image inpainting Magnetoencephalography Signal enhancement Surface reconstruction | ||
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How to Cite
1.
Arabian H, Karimian A, Marateb HR, Migliorelli C, Mañanas M, Romero S, Russi A, Nowak R. Reconstruction of Low-Quality Channel Data in Magnetoencephalography using Surface Reconstruction and Interpolation Methods. Frontiers Biomed Technol. 2026;13(3):608-619.
