Brain Electric/Magnetic Tomography (BET /BMT) consists of a 3D-image reconstruction of the Primary Current Density inside the brain from the signals measured outside the head, which can be considered as a functional neuroimaging modality. Mathematically, this is known as the EEG/MEG inverse problem (IP), which is ill posed (non-unique solution). To find a unique BET/BMT, additional information and proper models are needed, which has led to the development of several different methods for solving the IP.

In this work we introduce Neuronic Source Localizer, which include some of the methods reported in the literature for computing BET/BMT, such as: Minimum Norm, Weighted Minimum Norm, Low Resolution Tomography (LORETA) and Bayesian Model Averaging. The system works with EEG and MEG data, in time and frequency domain.

This system is a useful tool for cognitive and clinical researchers who study normal and abnormal brain processes such as cognition and epilepsy because it makes easier to compute BET/BMT in a few steps.