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

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

doi:10.3389/fnins.2022.984036

Cited by 10 publications

(2 citation statements)

References 31 publications

(40 reference statements)

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“…A three-dimensional laser scanner (HSCAN Prince 775, Scantech Inc., Hangzhou, China) was used to scan the participant wearing the helmet. Co-registration was performed using the OMMR Toolbox [32] to obtain the position (Figure 2b) of the sensor relative to the brain space.…”

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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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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“…The anatomical structure with 1.0mm 3 spatial resolution was obtained through the anatomical magnetic resonance imaging. Then the OMMR toolbox [40] was used for coregistration between the 3D digitisations and anatomical structure. Finally, the positions and directions of the OPM sensors relative to brain were obtained, as shown in Fig.…”

Section: E Coregistration and Source Localizationmentioning

confidence: 99%

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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Spatiotemporal extended homogeneous field correction method for reducing complex interference in OPM-MEG

Zhao,

Wang,

Gao

et al. 2024

Biomedical Signal Processing and Control

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

Cited by 10 publications

References 31 publications

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

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

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

Spatiotemporal extended homogeneous field correction method for reducing complex interference in OPM-MEG

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