Brain Source Localization Using EEG Signal Analysis

Of the research areas devoted to biomedical sciences, the study of the brain remains a field that continually attracts interest due to the vast range of people afflicted with debilitating brain disorders and those interested in ameliorating its effects. To discover the roots of maladies and grasp the dynamics of brain functions, researchers and practitioners often turn to a process known as brain source localization, which assists in determining the source of electromagnetic signals from the brain. Aiming to promote both treatments and understanding of brain ailments, ranging from epilepsy and depression to schizophrenia and Parkinson's disease, the authors of this book provide a comprehensive account of current developments in the use of neuroimaging techniques for brain analysis. Their book addresses a wide array of topics, including EEG forward and inverse problems, the application of classical MNE, LORETA, Bayesian based MSP, and its modified version, M-MSP. Within the ten chapters that comprise this book, clinicians, researchers, and field experts concerned with the state of brain source localization will find a store of information that can assist them in the quest to enhance the quality of life for people living with brain disorders.

Dr. Munsif Ali Jatoi is a post-doctoral researcher in neuro signal processing at University Teknologi PETRONAS, Malaysia. He has conducted research and instruction in neuro signal processing at the University of Warwick (UK) and in private industry. Additionally, Dr. Jatoi has contributed a chapter in "EEG/ERG Analysis: Methods and Applications", (CRC Press, 2014). Professor Nidal Kamel is a Senior Member of IEEE, with expertise in signal processing, digital and analogue communication, and neural signal processing. He is currently an Associate Professor in the Department of Electrical Engineering, University Teknologi PETRONAS (Malaysia)

Introduction. Neuroimaging Techniques for Brain Analysis. EEG Forward Problem-1:Mathematical Background. EEG Forward Problem-II: Head Modeling Approaches. EEG Inverse Problem I: Classical Techniques. EEG Inverse Problem II: Hybrid Techniques. EEG Inverse Problem III: Subspace Based Techniques. EEG Inverse Problem IV: Baysesian Techniques. EEG Inverse Problem-V: Results and Comparison. Future Directions for EEG Source Localization. References. Appendix A: Pseudo-Codes for Classical and Modern Techniques. Appendix B: MATLAB Code for Implementation. Appendix C: Listing of Software for EEG Signal Processing.