An Effective Method to Repair Poor Signal of Magnetoencephalography Channel Data

Hanie  Arabian; Alireza  Karimian; Hamid Reza  Marateb; Carolina  Migliorelli; Miquel Angel  Mañanas; Sergio  Romero; Antonio  Russi; Rafał  Nowak

doi:10.18502/fbt.v13i2.21940

Hanie Arabian Department of Biomedical Engineering, Faculty of Engineering, University of Isfahan, Isfahan, Iran
Alireza Karimian Department of Biomedical Engineering, Faculty of Engineering, University of Isfahan, Isfahan, Iran
Hamid Reza Marateb Department of Biomedical Engineering, Faculty of Engineering, University of Isfahan, Isfahan, Iran
Carolina Migliorelli Unit of Digital Health, Eurecat, Centre Tecnològic de Catalunya, 08005 Barcelona, Spain
Miquel Angel Mañanas Department of Automatic Control, Biomedical Engineering Research Center, Polytechnic University of Catalonia, BarcelonaTech (UPC), Barcelona, Spain
Sergio Romero Department of Automatic Control, Biomedical Engineering Research Center, Polytechnic University of Catalonia, BarcelonaTech (UPC), Barcelona, Spain
Antonio Russi Epilepsy Unit, Hospital Quirón Teknon, Barcelona, Spain
Rafał Nowak Magnetoencephalography Unit, Hospital Quirón Teknon, Barcelona, Spain

DOI: https://doi.org/10.18502/fbt.v13i2.21940

Keywords: Data Inpainting; Data Quality Enhancement; Magnetoencephalography; Signal Reconstruction

Abstract

Purpose: Magnetoencephalography is the recording of magnetic fields resulting from the activities of brain neurons and provides the possibility of direct measurement of their activity in a non-invasive manner. Despite its high spatial and temporal resolution, magnetoencephalography has a weak amplitude signal, drastically reducing the signal-to-noise ratio in case of environmental noise. Therefore, signal reconstruction methods can be effective in recovering noisy and lost information.

Materials and Methods: The magnetoencephalography signal of 11 healthy young subjects was recorded in a resting state. Each signal contains the data of 148 channels which were fixed on a helmet. The performance of three different reconstruction methods has been investigated by using the data of adjacent channels from the selected track to interpolate its information. These three methods are the surface reconstruction methods, partial differential equations algorithms, and finite element-based methods. Afterward to evaluate the performance of each method, R-square, root mean square error, and signal-to-noise ratio between the reconstructed signal and the original signal were calculated. The relation between these criteria was checked through proper statistical tests with a significance level of 0.05.

Results: The mean method with the root mean square error of 0.016 ± 0.009 (mean ± SD) at the minimum time (3.5 microseconds) could reconstruct an epoch. Also, the median method with a similar error but in 5.9 microseconds with a probability of 99.33% could reconstruct an epoch with an R-square greater than 0.7.

Conclusion: The mean and median methods can reconstruct the noisy or lost signal in magnetoencephalography with a suitable percentage of similarity to the reference by using the signal of adjacent channels from the damaged sensor.