BRAIN SOURCE LOCALIZATION TECHNIQUE FOR EEG SIGNALS BASED ON ENHANCED MULTIPLE SPARSE PRIORS - MUNSIF , ALI JATOI (2016)
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.