Multiple Source Detection based on Spatial Clustering and Its Applications on Wearable OPM-MEG

Objective: Magnetoencephalography (MEG) is a non-invasive technique that measures the magnetic fields of brain activity. In particular, a new type of optically pumped magnetometer (OPM)-based wearable MEG system has been developed in recent years. Source localization in MEG can provide signals and l...

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Bibliographic Details
Published in:IEEE transactions on biomedical engineering Vol. 69; no. 10; p. 1
Main Authors: An, Nan, Cao, Fuzhi, Li, Wen, Wang, Wenli, Xu, Weinan, Wang, Chunhui, Gao, Yang, Xiang, Min, Ning, Xiaolin
Format: Journal Article
Language:English
Published: United States IEEE 01.10.2022
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
Algorithms
Brain
Clustering
Clustering algorithms
Dipoles
Distribution functions
Eigenvalues and eigenfunctions
Graphical models
inverse problem
Inverse problems
Localization
Location awareness
Magnetic fields
Magnetoencephalography
Magnetometers
multiple source localization
Nervous system
OPM-MEG
parametric method
Particle swarm optimization
Sensory stimuli
spatial clustering
Spatial distribution
Wearable technology
ISSN:0018-9294, 1558-2531, 1558-2531
Online Access:Get full text
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Summary:Objective: Magnetoencephalography (MEG) is a non-invasive technique that measures the magnetic fields of brain activity. In particular, a new type of optically pumped magnetometer (OPM)-based wearable MEG system has been developed in recent years. Source localization in MEG can provide signals and locations of brain activity. However, conventional source localization methods face the difficulty of accurately estimating multiple sources. The present study presented a new parametric method to estimate the number of sources and localize multiple sources. In addition, we applied the proposed method to a constructed wearable OPM-MEG system. Methods: We used spatial clustering of the dipole spatial distribution to detect sources. The spatial distribution of dipoles was obtained by segmenting the MEG data temporally into slices and then estimating the parameters of the dipoles on each data slice using the particle swarm optimization algorithm. Spatial clustering was performed using the spatial-temporal density-based spatial clustering of applications with a noise algorithm. The performance of our approach for detecting multiple sources was compared with that of four typical benchmark algorithms using the OPM-MEG sensor configuration. Results: The simulation results showed that the proposed method had the best performance for detecting multiple sources. Moreover, the effectiveness of the method was verified by a multimodel sensory stimuli experiment on a real constructed 31-channel OPM-MEG. Conclusion: Our study provides an effective method for the detection of multiple sources. Significance: With the improvement of the source localization methods, MEG may have a wider range of applications in neuroscience and clinical research.
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ISSN:0018-9294
1558-2531
1558-2531
DOI:10.1109/TBME.2022.3161830