Detection and analysis of MEG signals in occipital region with double-channel OPM sensors

Zhang, Xin; Chen, Chunqiao; Zhang, Mingkang; Ma, Changyu; Zhang, Yin; Wang, Hui; Guo, Qingqian; Hu, Tao; Liu, Zhaobang; Chang, Yan; Hu, Kejia; Yang, Xiaodong

doi:10.1016/j.jneumeth.2020.108948

Cited by 17 publications

(8 citation statements)

References 19 publications

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“…Yet, OPM technology is currently under development; therefore, the high cost and the requirement of magnetic shielding still limit its use to research context. However, in view of the promising advancement of wearable OPM systems for MEG application (Boto et al, 2019;Roberts et al, 2019;Boto et al, 2021;Seymour et al, 2021), and the progress in development of OPM-optimized magnetic shielding solutions (Holmes et al, 2018;Iivanainen et al, 2019;Borna et al, 2020;Zhang et al, 2020), it is foreseeable a broader diffusion of the technology in the future with a consequential decrease of the costs. This would paved the way toward the development of wearable OPM-MMG systems (Zuo et al, 2020), for the measurement and assessment of muscle activity for research and diagnostic purposes.…”

Section: Discussionmentioning

confidence: 99%

Muscle Fatigue Revisited – Insights From Optically Pumped Magnetometers

et al. 2021

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So far, surface electromyography (sEMG) has been the method of choice to detect and evaluate muscle fatigue. However, recent advancements in non-cryogenic quantum sensors, such as optically pumped magnetometers (OPMs), enable interesting possibilities to flexibly record biomagnetic signals. Yet, a magnetomyographic investigation of muscular fatigue is still missing. Here, we simultaneously used sEMG (4 surface electrode) and OPM-based magnetomyography (OPM-MMG, 4 sensors) to detect muscle fatigue during a 3 × 1-min isometric contractions of the left rectus femoris muscle in 7 healthy participants. Both signals exhibited the characteristic spectral compression distinctive for muscle fatigue. OPM-MMG and sEMG slope values, used to quantify the spectral compression of the signals, were positively correlated, displaying similarity between the techniques. Additionally, the analysis of the different components of the magnetic field vector enabled speculations regarding the propagation of the muscle action potentials (MAPs). Altogether these results show the feasibility of the magnetomyographic approach with OPMs and propose a potential alternative to sEMG for the study of muscle fatigue.

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Section: Discussionmentioning

confidence: 99%

Muscle Fatigue Revisited – Insights From Optically Pumped Magnetometers

et al. 2021

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“…The use of neuroimaging techniques, such as fMRI [26], [33], electroencephalography [34] and magnetoencephalography [35], [36], allows indirect assessments of brain neuronal activity. However, it is not straightforward to extract the pure neuronal signal as neuroimaging signals are affected by physiological influences and it is essential to consider this phenomenon when developing a classification algorithm.…”

Section: A Related Workmentioning

confidence: 99%

Non-Oscillatory Connectivity Approach for Classification of Autism Spectrum Disorder Subtypes Using Resting-State fMRI

et al. 2022

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Resting-state functional magnetic resonance imaging (rs-fMRI) is an efficient tool to measure brain connectivity and it can reveal patterns that distinguish autism spectrum disorder (ASD) from normal controls (NC). It is established that the fractal nature of neuroimaging signals will affect the estimation of brain's functional connectivity. Therefore, the ordinary correlation of rs-fMRI may not provide the original neuronal activity of the brain. In this work, the non-oscillatory brain connectivity method is proposed to distinguish subtypes of ASD from NC. The three subtypes of ASD namely autistic disorder (ATD), Asperger's disorder (APD), and Pervasive developmental disorder-not other specified (PDD) are classified from NC by extracting the non-oscillatory connectivity from the BOLD rs-fMRI signal. A number of significant connections are extracted by utilizing the p-value analysis and these significant connections are fed to machine learning (ML) classifiers for classification of ASD subtypes against normal control. The performance for binary classification is recorded at accuracy of 98.6%, 97.2%, 97.2%, respectively, for ATD vs. NC, APD vs. NC and PDD vs. NC. Whereas, for multiclass (ATD, APD, PDD and NC), the best accuracy is 88.9%. Both binary and multiclass classification outperformed the conventional Pearson correlation-based connectivity and benchmark approaches in terms of accuracy, sensitivity, specificity. This work demonstrates the great potential of non-oscillatory connectivity approaches, not only for autism diagnosis but also for other neurological disorders.

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“…Compared with the SQUID-MEG approach, the unique advantage of the OPM-MEG approach is that its sensors can be closer to the scalp, allowing it to obtain high-magnitude signals 18 , 19 . The rigid helmet is usually designed with sensor slots without position constraints, to allow the sensor depths to be freely adjusted.…”

Section: Co-registration and Involved Coordinate Systemsmentioning

confidence: 99%