Fundamentals of MEG and EEG Biophysics, Instrumentation, and Data Analysis

Langue : Anglais

Auteurs : Hamalainen Matti, Ilmoniemi Risto, Parkkonen Lauri

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Fundamentals of MEG and EEG: Biophysics, Instrumentation, and Data Analysis gives graduate students and researchers a technical understanding of the fundamentals of MEG and EEG that will enable them to:

Gain expertise in the state-of-the-art of MEG and EEG
Understand the generation, measurement, and modeling of electromagnetic brain signals
Understand the relationship of MEG/EEG with other brain imaging methods
Design MEG / EEG measurement systems and evaluate their performance
Develop and evaluate data analysis methods

A well-integrated and systematic presentation of the principles and their application, using a common notation
Authored by the leading people in the field
On-line examples implemented in (MNE-) Python

1. Introduction to MEG/EEG 2. Signal generation 3. Instrumentation 4. Data acquisition 5. Experimental design 6. Signal processing 7. Source estimation 8. Connectivity estimation 9. Multimodal imaging

Dr. Hämäläinen is Professor of Radiology at Massachusetts General hospital and Director of Magnetoencephalography (MEG) Core at Athinoula A. Martinos Center for Biomedical Imaging at MGH. He is one of the pioneers in the application of MEG in conjunction with other non-invasive functional and anatomical imaging methods to study human brain function. He has had a crucial role in developing whole-head MEG instrumentation, analytical methods and tools, as well as experimental protocols, which have together paved the way for MEG becoming an important basic research and clinical tool worldwide.
In 1993 he was the co-author of a seminal review article on MEG in Reviews of Modern Physics, now with more than 4000 citations. His current research interests include further development of anatomically-constrained MEG/EEG source estimation methods, including sparse and temporally continuous approaches, combination of non-invasive and invasive electromagnetic source imaging with hemodynamic measures, MEG/EEG studies of early brain development in infants, and adapting and extending MEG/EEG analysis methods to be applicable in real time in clinical neurophysiology studies.

Presents a well-integrated and systematic presentation of the principles and their application using common notation
Authored by the leading people in the field
Includes online examples implemented in (MNE-) Python