ISBN13: | 9781032622477 |
ISBN10: | 1032622474 |
Binding: | Hardback |
No. of pages: | 480 pages |
Size: | 234x156 mm |
Language: | English |
Illustrations: | 68 Illustrations, black & white; 38 Illustrations, color; 17 Halftones, black & white; 29 Halftones, color; 51 Line drawings, black & white; 9 Line drawings, color; 6 Tables, black & white |
700 |
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The Physics and Mathematics of Electroencephalogram
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This book focuses on a systematic introduction to the knowledge of mathematics and physics of EEG and discusses an in-depth application of EEG and the development of new methods and technologies for mining and analyzing EEG.
This book focuses on a systematic introduction to the knowledge of mathematics and physics of electroencephalogram (EEG) and discusses an in-depth application of EEG and the development of new methods and technologies for mining and analyzing EEG.
The Physics and Mathematics of Electroencephalogram offers a systematic overview of the technology for brain function and disease. It covers five parts: background knowledge of EEG, EEG forward problems, high-resolution EEG imaging, EEG inverse problems, and EEG reference electrode. The author reviews the critical technologies in brain function and disease, such as EEG sourcing, EEG imaging, and EEG reference electrode standardization technique. The book?s aim is to clarify the mechanism of EEG from the perspective of physics, mathematics, and engineering science to help multidisciplinary readers better understand and use EEG information more effectively.
This book can be used as reference for researchers in the fields of neuroengineering, cognitive neuroscience, neurology, psychiatry, applied mathematics, and brain-like intelligence.
1 Overview of EEG
2 Electromagnetics behind Brain Electric Field
3 Biophysics and Source Models of EEG
4 Brain Electrical Field in Regular Head Model
5 Brain Electric Field in Realistic Head Model
6 Theory of Equivalent Distributed Sources
7 High-Resolution Cortical Imaging
8 Scalp Laplacian Imaging
9 A Unified Framework for High-Resolution EEG
10 Basic Theory of EEG Inverse Problem
11 Signal Space-Based EEG Inverse Solution
12 Iterative Minimum Norm Solution
13 Zero-Reference for Scalp EEG
14 EEG Reference Selection