Optimal design of on‐scalp electromagnetic sensor arrays for brain source localisation

Abstract

Optically pumped magnetometers (OPMs) are quickly widening the scopes of noninvasive neurophysiological imaging. The possibility of placing these magnetic field sensors on the scalp allows not only to acquire signals from people in movement, but also to reduce the distance between the sensors and the brain, with a consequent gain in the signal-to-noise ratio. These advantages make the technique particularly attractive to characterise sources of brain activity in demanding populations, such as children and patients with epilepsy. However, the technology is currently in an early stage, presenting new design challenges around the optimal sensor arrangement and their complementarity with other techniques as electroencephalography (EEG). In this article, we present an optimal array design strategy focussed on minimising the brain source localisation error. The methodology is based on the Cramér-Rao bound, which provides lower error bounds on the estimation of source parameters regardless of the algorithm used. We utilise this framework to compare whole head OPM arrays with commercially available electro/magnetoencephalography (E/MEG) systems for localising brain signal generators. In addition, we study the complementarity between EEG and OPM-based MEG, and design optimal whole head systems based on OPMs only and a combination of OPMs and EEG electrodes for characterising deep and superficial sources alike. Finally, we show the usefulness of the approach to find the nearly optimal sensor positions minimising the estimation error bound in a given cortical region when a limited number of OPMs are available. This is of special interest for maximising the performance of small scale systems to ad hoc neurophysiological experiments, a common situation arising in most OPM labs.

Transforming and comparing data between standard SQUID and OPM-MEG systems

“…The transformation method presented in this work could be used to plan measurements with OPM-MEG systems if prior SQUID-MEG results are available. The performance of source reconstruction and spatial resolution of an OPM-MEG system with a limited number of sensors is linked to the layout of sensor placement around the head [ 58 ]. An advanced method for selecting an optimal layout could be the statistically based lead selection algorithm, which was developed for the purpose of electrocardiography [ 50 ], and magnetocardiography [ 57 ].…”

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

“…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 ).…”

“…OPM-MEG is currently undergoing technical exploration. Researchers from various backgrounds have focused their energy on OPM-MEG system construction ( Borna et al., 2020 ), active shielding ( Iivanainen et al., 2019 ), helmet design ( Hill et al., 2020 ), sensor array design ( Beltrachini et al., 2021 ), co-registration with MRI ( Zetter et al., 2019 ; Cao et al., 2021 ), and applications ( Iivanainen et al., 2020 ; Boto et al., 2021 ; Wittevrongel et al., 2021 ). In this study, we constructed a 32-channel wearable OPM-MEG system and aimed to use it to analyze the SEFs of four subjects under median and ulnar nerve electrical stimulations.…”

Effcient magnetometer sensor array selection for signal reconstruction and brain source localization

“…In practice, both SQUID and OPM are used in sensor arrays placed around the head. To date, there have been some discussions into using a limited number of sensors [4,5], but only few methods exist to evaluate which sensors are most capable of reconstructing brain-wide activity or source localization [6,7]. We demonstrate that by exploiting low-dimensional patterns of brain activity along with a greedy selection algorithm, we can identify a small number of sensors that can accurately reconstruct brain signals and source localization.…”