Tikhonov Regularization Techniques in Simulated Brain Electrical Tomography

In order to solve the inverse electroencephalographic (EEG) problem, the unknown intracranial current sources must be calculated. The solution provides a tomographical representation of the brain activity. In the present work the quality of the solutions given by the optimized Tikhonov Regularization Technique (TRT) is assessed, in comparison to the Algebraic Reconstruction Techniques (ART). Results show that ART algorithms specifically designed to compensate for noisy data perform similarly with TRT, but require the prior knowledge of the characteristic of the noise affecting the data. This is not a prerequisite for the TRT method. Therefore, when TRT optimization criteria are successful, this methods may be used in real EEG data inversions providing tomographic solutions in a wider context than ART.