Data‐driven model optimization for optically pumped magnetometer sensor arrays

Duque-Muñoz, Leonardo; Tierney, Tim M.; Meyer, Sofie S.; Boto, Elena; Holmes, Niall; Roberts, Gillian; Leggett, James; Vargas-Bonilla, Jesús Francisco; Bowtell, Richard; Brookes, Matthew J.; López, José David; Barnes, Gareth R.

doi:10.1002/hbm.24707

Cited by 16 publications

(9 citation statements)

References 47 publications

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“…These gain errors would be analogous to random sensor orientation errors of ~8, 13, and 26 degrees respectively. Similar relationships between gain errors, positional and orientation errors for smaller OPM systems are documented elsewhere (Duque-Munoz et al, 2019). Figure 5.…”

Section: 3: the Impact Of Gain Errors On Spatial Discriminationsupporting

confidence: 76%

Pragmatic spatial sampling for wearable MEG arrays

Tierney

Mellor

O'Neill

et al. 2019

Preprint

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Several new technologies have recently emerged promising new MEG systems in which the sensors can be placed close to the scalp. One such technology, Optically Pumped Magnetometry MEG (OP-MEG) allows for a scalp mounted flexible system that provides field measurements within mm of the scalp surface. A question that arises in developing on-scalp systems, such as OP-MEG scanners, is: how many sensors are necessary to achieve adequate performance/spatial discrimination? There are many factors to consider in answering this question such as the signal to noise ratio (SNR), the locations and depths of the sources of interest, the density of spatial sampling, sensor gain errors (due to interference, subject movement, cross-talk, etc.) and, of course, the desired spatial discrimination. In this paper, we provide simulations which show the impact these factors have on designing sensor arrays for wearable MEG. While OP-MEG has the potential to provide high information content at dense spatial samplings, we find that adequate spatial discrimination of sources (<1cm) can be achieved with relatively few sensors (<100) at coarse spatial samplings (~30mm) at high SNR. Comparable discrimination for traditional cryogenic systems require far more channels by these same metrics. Finally we show that sensor gain errors have the greatest impact on discrimination between deep sources at high SNR.

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Section: 3: the Impact Of Gain Errors On Spatial Discriminationsupporting

confidence: 76%

Pragmatic spatial sampling for wearable MEG arrays

Tierney

Mellor

O'Neill

et al. 2019

Preprint

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“…To further improve the detection accuracy, optimization of the sensor array with a limited number for a specific subject is required accordingly. Research has been performed to consider the sensor configuration design from the perspective of source reconstruction accuracy ( Beltrachini et al., 2021 ; Duque-Muñoz et al., 2019 ). However, for practical application, designing and fabricating a personalized helmet for each subject is complex and time-consuming and many studies still use a general helmet for different subjects ( Iivanainen et al., 2020 ).…”

Section: Discussionmentioning

confidence: 99%

Imaging somatosensory cortex responses measured by OPM-MEG: Variational free energy-based spatial smoothing estimation approach

Cao

Wen

et al. 2022

iScience

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Summary In recent years, optically pumped magnetometer (OPM)-based magnetoencephalography (MEG) has shown potential for analyzing brain activity. It has a flexible sensor configuration and comparable sensitivity to conventional SQUID-MEG. We constructed a 32-channel OPM-MEG system and used it to measure cortical responses to median and ulnar nerve stimulations. Traditional magnetic source imaging methods tend to blur the spatial extent of sources. Accurate estimation of the spatial size of the source is important for studying the organization of brain somatotopy and for pre-surgical functional mapping. We proposed a new method called variational free energy-based spatial smoothing estimation (FESSE) to enhance the accuracy of mapping somatosensory cortex responses. A series of computer simulations based on the OPM-MEG showed better performance than the three types of competing methods under different levels of signal-to-noise ratios, source patch sizes, and co-registration errors. FESSE was then applied to the source imaging of the OPM-MEG experimental data.

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“…This may be slightly different from the physical orientation of the sensor due to the presence of cross-talk (Tierney, Holmes, et al, 2019) or imperfect on board coil design. However, such issues can be reduced by operating the sensors with coils specifically designed to reduce cross-talk (Nardelli, Krzyzewski, & Knappe, 2019) or by the use of data driven approaches which can learn sensor sensitive axes from the data (Duque-muñoz et al, 2019).…”

Section: Discussionmentioning

confidence: 99%

Modelling optically pumped magnetometer interference as a mean (magnetic) field

Alexander

Mellor

et al. 2020

Preprint

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Here we propose that much of the magnetic interference observed when using optically pumped magnetometers can be modeled spatially as a mean (magnetic) field. We show that this approximation reduces sensor level variability and substantially improves statistical power. This model does not require knowledge of the underlying neuroanatomy nor the sensor positions. It only needs information about the sensor orientation. Due to the model’s low rank there is little risk of removing substantial neural signal. However, we provide a framework to assess this risk for any sensor number, design or subject neuroanatomy. We find that the risk of unintentionally removing neural signal is reduced when multi-axis recordings are performed. We validated the method using a binaural auditory evoked response paradigm and demonstrated that the mean field correction increases reconstructed SNR in relevant brain regions in both the spatial and temporal domain. Considering the model’s simplicity and efficacy, we suggest that this mean field correction can be a powerful preprocessing step for arrays of optically pumped magnetometers.

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Data‐driven model optimization for optically pumped magnetometer sensor arrays

Cited by 16 publications

References 47 publications

Pragmatic spatial sampling for wearable MEG arrays

Pragmatic spatial sampling for wearable MEG arrays

Imaging somatosensory cortex responses measured by OPM-MEG: Variational free energy-based spatial smoothing estimation approach

Modelling optically pumped magnetometer interference as a mean (magnetic) field

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