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

An, Nan; Cao, Fuzhi; Li, Wen; Wang, Wenli; Xu, Weinan; Wang, Chunhui; Gao, Yang; Xiang, Min; Ning, Xiaolin

doi:10.1109/tbme.2022.3161830

Cited by 12 publications

(6 citation statements)

References 61 publications

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“…The results show that 28-channel OPM-MEG which covers the somatosensory cortex can measure source activation precisely. In addition, previous literatures [69], [70] has also demonstrated the feasibility and reliability of source estimation using about 30 OPM sensors with local layout on scalp. However, in this paper or other existing OPM-MEG systems [35], [71], the numbers of sensors are relatively small.…”

Section: Discussionmentioning

confidence: 90%

OPM-MEG Measuring Phase Synchronization on Source Time Series: Application in Rhythmic Median Nerve Stimulation

Ma,

Gao,

et al. 2024

IEEE Trans. Neural Syst. Rehabil. Eng.

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The magnetoencephalogram (MEG) based on array optically pumped magnetometers (OPMs) has the potential of replacing conventional cryogenic superconducting quantum interference device. Phase synchronization is a common method for measuring brain oscillations and functional connectivity. Verifying the feasibility and fidelity of OPM-MEG in measuring phase synchronization will help its widespread application in the study of aforementioned neural mechanisms. The analysis method on source-level time series can weaken the influence of instantaneous field spread effect. In this paper, the OPM-MEG was used for measuring the evoked responses of 20Hz rhythmic and arrhythmic median nerve stimulation, and the inter-trial phase synchronization (ITPS) and inter-reginal phase synchronization (IRPS) of primary somatosensory cortex (SI) and secondary somatosensory cortex (SII) were analysed. The results find that under rhythmic condition, the evoked responses of SI and SII show continuous oscillations and the effect of resetting phase. The values of ITPS and IRPS significantly increase at the stimulation frequency of 20Hz and its harmonic of 40Hz, whereas the arrhythmic stimulation does not exhibit this phenomenon. Moreover, in the initial stage of stimulation, the ITPS and IRPS values are significantly higher at Mu rhythm in the rhythmic condition compared to arrhythmic. In conclusion, the results demonstrate the ability of OPM-MEG in measuring phase pattern and functional connectivity on source-level, and may also prove beneficial for the study on the mechanism of rhythmic stimulation therapy for rehabilitation.

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Section: Discussionmentioning

confidence: 90%

OPM-MEG Measuring Phase Synchronization on Source Time Series: Application in Rhythmic Median Nerve Stimulation

Ma,

Gao,

et al. 2024

IEEE Trans. Neural Syst. Rehabil. Eng.

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“…Our previous work quantified and evaluated the performance of each co-registration method ( Cao et al, 2021 ). In addition, we have applied the most accurate co-registration method, the laser scanner, in real experiments measuring the somatosensory evoked fields ( An et al, 2022a , b ). The validity of each co-registration method is verified.…”

Section: Discussionmentioning

confidence: 99%

OMMR: Co-registration toolbox of OPM-MEG and MRI

Cao

et al. 2022

Front. Neurosci.

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Magnetoencephalography (MEG) based on optically pumped magnetometers (OPM-MEG) has shown better flexibility in sensor configuration compared with the conventional superconducting quantum interference devices-based MEG system while being better suited for all-age groups. However, this flexibility presents challenges for the co-registration of MEG and magnetic resonance imaging (MRI), hindering adoption. This study presents a toolbox called OMMR, developed in Matlab, that facilitates the co-registration step for researchers and clinicians. OMMR integrates the co-registration methods of using the electromagnetic digitization system and two types of optical scanners (the structural-light and laser scanner). As the first open-source co-registration toolbox specifically for OPM-MEG, the toolbox aims to standardize the co-registration process and set the ground for future applications of OPM-MEG.

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“…The internal brain space was obtained by segmenting the T1-MRI files from a 26-yearold male participant. Thirty-one OPM sensors installed on an 85-channel rigid helmet were simulated as the sensor array [31]. The locations of the sensors on the helmet were optimized for the somatosensory-evoked experiment.…”

Section: Simulation Experimentsmentioning

confidence: 99%

Automatic Estimation of the Interference Subspace Dimension Threshold in the Subspace Projection Algorithms of Magnetoencephalography Based on Evoked State Data

Zhao,

Wang,

Gao

et al. 2024

Bioengineering

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A class of algorithms based on subspace projection is widely used in the denoising of magnetoencephalography (MEG) signals. Setting the dimension of the interference (external) subspace matrix of these algorithms is the key to balancing the denoising effect and the degree of signal distortion. However, most current methods for estimating the dimension threshold rely on experience, such as observing the signal waveforms and spectrum, which may render the results too subjective and lacking in quantitative accuracy. Therefore, this study proposes a method to automatically estimate a suitable threshold. Time–frequency transformations are performed on the evoked state data to obtain the neural signal of interest and the noise signal in a specific time–frequency band, which are then used to construct the objective function describing the degree of noise suppression and signal distortion. The optimal value of the threshold in the selected range is obtained using the weighted-sum method. Our method was tested on two classical subspace projection algorithms using simulation and two sensory stimulation experiments. The thresholds estimated by the proposed method enabled the algorithms to achieve the best waveform recovery and source location error. Therefore, the threshold selected in this method enables subspace projection algorithms to achieve the best balance between noise removal and neural signal preservation in subsequent MEG analyses.

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

Cited by 12 publications

References 61 publications

OPM-MEG Measuring Phase Synchronization on Source Time Series: Application in Rhythmic Median Nerve Stimulation

OPM-MEG Measuring Phase Synchronization on Source Time Series: Application in Rhythmic Median Nerve Stimulation

OMMR: Co-registration toolbox of OPM-MEG and MRI

Automatic Estimation of the Interference Subspace Dimension Threshold in the Subspace Projection Algorithms of Magnetoencephalography Based on Evoked State Data

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