Estimation of the Shape of Neural Activity Sources in Neuromagnetic Inverse Problems

The location and area shape of neural activity sources were estimated using machine learning. In an inverse problem analysis to measure the magnetic field using a magnetic sensor and estimate the location of the current dipole (CD), eight magnetic sensors were used, and the percentage of location estimation errors of 1 mm or less was 43.2%, and the percentage of errors of 10 mm or more was 2.4%. In particular, the accuracy of location estimation was high on the z = 6 cm plane, and the percentage of errors of 1 mm or less increased. This shows high accuracy compared to the spatial resolution ability (5 to 7 mm), suggesting its effectiveness in estimating the area of neural activity sources.