Fast Acquisition of Sensor Array Geometry of Whole-Head Magnetoencephalograph Systems Using a Neural Network

Abstract

Acquiring position, orientation, and sensitivity of magnetometers in a helmet-shaped sensor array is crucial for accurate current source reconstruction in magnetoencephalography. To determine these parameters for each magnetometer, we utilize a spherical calibration coil array. In our previous study, the position and orientation of each magnetometer were determined as the solution of an inverse problem through a numerical search that minimized the difference between the theoretical magnetic field signals from each coil and the measured signals detected by the magnetometer. In this study, we applied a deep neural network to estimate the position and orientation of each magnetometer in the helmet-shaped sensor array without solving the inverse problem. A total of 223 million pairs of a given magnetometer's five parameters (x, y, z, θ, and ϕ) and the corresponding theoretical magnetic field signals from the coils were used to train the neural network. The training process required approximately 53 h using a commercially available GPU-equipped computer. The trained neural network was then applied to acquire the sensor geometry from magnetic field data obtained during a conventional calibration procedure for a 160-channel whole-head magnetoencephalograph system using a spherical calibration coil array. The position and orientation of each magnetometer estimated by this method deviated by an average of 0.65 mm and 0.51°, respectively, from those obtained via the conventional inverse problem approach. The acquisition of the geometry for all 160 magnetometers required less than 8 ms. With such high-speed acquisition, this approach opens possibilities for future applications in acquiring positional information of wearable sensor arrays whose structures change in real time.