BRAIN SOURCE LOCALIZATION TECHNIQUE FOR EEG SIGNALS BASED ON ENHANCED MULTIPLE SPARSE PRIORS

The brain source localization information is used to diagnose various brain disorders
such as epilepsy, schizophrenia, stress, depression and Alzheimer. It is an ill-posed
problem in nature affected by uncertainty in solution. Different algorithms are
proposed for the solution of this ill-posed problem which include minimum norm
estimation (MNE), second order Laplacian based low resolution brain electromagnetic
tomography (LORETA), standardized LORETA (sLORETA), exact LORETA,
subspace based multiple signal classifier (MUSIC), Beamformer and Bayesian
framework based multiple source priors (MSP). The solution provided by each of the
algorithms mentioned above is characterized by various parameters which include the
accuracy, computational complexity and localization error. The existing algorithms
suffer from low resolution (LORETA family), high computational time (subspace
algorithms and FOCUSS, WMN-LORETA) and no validation.