In recent years, many kinds of magnetic sensors have been developed for biomagnetic measurement, such as magnetocardiography (MCG) and magnetoencephalography (MEG). However, it is difficult to evaluate their performance using only actual MCG or MEG measurements. In this paper, we propose the use of the calibrated MEG phantom for quantitative evaluation of magnetic sensors and present the experimental method. We choose a magneto-impedance (MI) sensor as an example of the magnetic sensor to be evaluated. The magnetic field distribution near the phantom was measured using the MI sensor and a signal source was localized with different averaging numbers and different signal source intensities. The results suggest that the MEG signal cannot be observed in the usual averaging time (i.e., 100), even when the sensor is located near the head; 4.0 mm of source localization accuracy can be achieved with 400-times averaging if the sensor noise decreases to 1/10. The use of the calibrated phantom, instead of examination with human subjects, is effective for quantitative evaluation of the performance of magnetic sensors.

  In this study, cardiac source localization was simulated using the spatial filter method. Three types of spatial filters were obtained using the standardized low-resolution brain electromagnetic tomography (sLORETA) method, based on different examination procedures. In Type A filter, the examination was conducted at the front of the torso. In both Type B and Type C filters, the examinations were conducted at the front and back of the torso; however, the distance from the frontal observation plane to the center of the heart model was different for each type. In the simulation experiments, first the goodness of fit (GOF) value was introduced to determine the proper threshold for each spatial filter. Then, single and multiple dipole sources were simulated at different depths with and without noise. The extension of the solutions computed using these spatial filters was investigated. Finally, the performances of these spatial filters, with the corresponding averaged thresholds, were evaluated using the GOF. Type B and Type C spatial filters demonstrated reduction in the extension of source dependency on source depth and improvement in the accuracy of source localization with noisy data.