In the last ten years many different brain imaging devices have conveyed a lot of information about the brain functioning in different experimental conditions. In every case, the biomedical engineers, together with mathematicians, physicists and physicians are called to elaborate the signals related to the brain activity in order to extract meaningful and robust information to correlate with the external behavior of the subjects. In such attempt, different signal processing tools used in telecommunications and other field of engineering or even social sciences have been adapted and re-used in the neuroscience field. The present book would like to offer a short presentation of several methods for the estimation of the cortical connectivity of the human brain. The methods here presented are relatively simply to implement, robust and can return valuable information about the causality of the activation of the different cortical areas in humans using non invasive electroencephalographic recordings. The knowledge of such signal processing tools will enrich the arsenal of the computational methods that a engineer or a mathematician could apply in the processing of brain signals. Table of Contents: Introduction / Estimation of the Effective Connectivity from Stationary Data by Structural Equation Modeling / Estimation of the Functional Connectivity from Stationary Data by Multivariate Autoregressive Methods / Estimation of Cortical Activity by the use of Realistic Head Modeling / Application: Estimation of Connectivity from Movement-Related Potentials / Application to High-Resolution EEG Recordings in a Cognitive Task (Stroop Test) / Application to Data Related to the Intention of Limb Movements in Normal Subjects and in a Spinal Cord Injured Patient / The Instantaneous Estimation of the Time-Varying Cortical Connectivity by Adaptive Multivariate Estimators / Time-Varying Connectivity from Event-Related Potentials

Autorentext

Laura Astolfi is a researcher at the Department of Human Physiology and Pharmacology of the University of Rome "La Sapienza" and at the Neuro[1]physiopathology Unit of the Fondazione Santa Lucia, Rome. Her research interests involve the study of the cortical connectivity, the neuroelectrical inverse problem and the multimodal integration of fMRI and HR EEG data. She received the degree in Electronic Engineering cum laude from the University of Rome "La Sapienza" in 2003 and the PhD in Biomedical Engineering from the University of Bologna in 2007. Fabio Babiloni was born in Rome in 1961. He got the master degree in Electronic Engineering at the University of Rome "La Sapienza" summa cum laude in 1986, and the PhD in Computational Engineering at the Helsinki University of Technology, Helsinki in the 2000 with a dissertation on the multimodal integration of EEG and fMRI. He is currently Associate Professor of Human Physiology at the Faculty of Medicine of the University of Rome "La Sapienza", Rome, Italy. He is author of more that 85 papers on bioengineering and neurophysiological topics on international peer-reviewed scientific journals, and more than 200 contributions to conferences and books chapter. Currents interest are in the field of multimodal integration of EEG, MEG and fMRI data, cortical connectivity estimation and Brain Computer Interface. Prof. Babiloni is currently Associate Editor of four scientific Journals "Clinical Neurophysiology", "International Journal of Bioelectromagnetism","IEEE Trans. On Neural System and Rehabilitation Engineering", and "Computational Intelligence and Neuroscience".

Inhalt
Introduction.- Estimation of the Effective Connectivity from Stationary Data by Structural Equation Modeling.- Estimation of the Functional Connectivity from Stationary Data by Multivariate Autoregressive Methods.- Estimation of Cortical Activity by the use of Realistic Head Modeling.- Application: Estimation of Connectivity from Movement-Related Potentials.- Application to High-Resolution EEG Recordings in a Cognitive Task (Stroop Test).- Application to Data Related to the Intention of Limb Movements in Normal Subjects and in a Spinal Cord Injured Patient.- The Instantaneous Estimation of the Time-Varying Cortical Connectivity by Adaptive Multivariate Estimators.- Time-Varying Connectivity from Event-Related Potentials.