000			03961nam a22003615i 4500
001			394091
003			ES-MaUEC
005			20230102123052.0
007			cr nn 008mamaa
008			221001s2022 sz | s |||| 0|eng d
020			_a9783030749187
024	7		_a10.1007/978-3-030-74918-7 _2doi
040			_aES-MaUEC _bspa _cES-MaUEC
100	1		_aKnösche, Thomas R _eautor _4aut _4http://id.loc.gov/vocabulary/relators/aut
245	1	0	_aEEG/MEG Source Reconstruction _bTextbook for Electro-and Magnetoencephalography _cby Thomas R Knösche, Jens Haueisen
250			_a1st edition 2022
264		1	_aCham _bSpringer International Publishing _c2022
300			_a1 recurso en línea (XIX, 415 páginas) _b174 ilustraciones a color
336			_atexto _btxt _2rdacontent
337			_aelectrónico _bc _2rdamedia
338			_arecurso electrónico _bcr _2rdacarrier
347			_aarchivo de texto _bPDF
505	0		_aChapter 1: Introduction -- Chapter 2: Neural Tissue and its Signals -- Chapter 3: Measurements -- Chapter 4: Source Models -- Chapter 5: Forward Models -- Chapter 6: Inverse Methods -- Chapter 7: Assessment -- Chapter 8: Applications.
520			_aThis textbook provides a comprehensive and didactic introduction from the basics to the current state of the art in the field of EEG/MEG source reconstruction. Reconstructing the generators or sources of electroencephalographic and magnetoencephalographic (EEG/MEG) signals is an important problem in basic neuroscience as well as clinical research and practice. Over the past few decades, an entire theory, together with a whole collection of algorithms and techniques, has developed. In this textbook, the authors provide a unified perspective on a broad range of EEG/MEG source reconstruction methods, with particular emphasis on their respective assumptions about sources, data, head tissues, and sensor properties. An introductory chapter highlights the concept of brain imaging and the particular importance of the neuroelectromagnetic inverse problem. This is followed by an in-depth discussion of neural information processing and brain signal generation and an introduction to the practice of data acquisition. Next, the relevant mathematical models for the sources of EEG and MEG are discussed in detail, followed by the neuroelectromagnetic forward problem, that is, the prediction of EEG or MEG signals from those source models, using biophysical descriptions of the head tissues and the sensors. The main part of this textbook is dedicated to the source reconstruction methods. The authors present a theoretical framework of the neuroelectromagnetic inverse problem, centered on Bayes' theorem, which then serves as the basis for a detailed description of a large variety of techniques, including dipole fit methods, distributed source reconstruction, spatial filters, and dynamic source reconstruction methods. The final two chapters address the important topic of assessment, including verification and validation of source reconstruction methods, and their actual application to real-world scientific and clinical questions. This book is intended as basic reading for anybody who is engaged with EEG/MEG source reconstruction, be it as a method developer or as a user, including advanced undergraduate students, PhD students, and postdocs in neuroscience, biomedical engineering, and related fields. .
700	1		_aHaueisen, Jens _eautor _4aut _4http://id.loc.gov/vocabulary/relators/aut
776	0	8	_iPrinted edition: _z9783030749163
776	0	8	_iPrinted edition: _z9783030749170
856	4	0	_uhttps://go.openathens.net/redirector/universidadeuropea.es?url=https://doi.org/10.1007/978-3-030-74918-7 _zAcceso a este recurso digital (usuarios Universidad Europea de Madrid)
942			_2lcc _cLE
988			_aSpringer_BiomedLife_2022
999			_c394091 _d394091