Combined Eeg In Research And Diagnostics Novel Perspectives And Improvements

Combined EEG in research and diagnostics: novel perspectives and improvements

Lateralized Brain Functions

This second edition volume expands on the previous edition with updates on the latest techniques used to study brain and behavioral laterality in both human and non-human animals. The chapters in this book cover numerous topics such as methods of measuring lateralization in a range of species by scoring behavior elicited by inputs to one of both brain hemispheres; behavioral methods to study motor preferences and lateralization in invertebrates; neurological methods to reveal lateralization; imaging and electrocephalographic techniques and transcranial stimulation; and new genetic approaches to studying lateralization in humans and zebrafish, and the roles of genes in the establishment and development of brain asymmetry. In the Neuromethods series style, chapters include the kind of detail and key advice from the specialists needed to get successful results in your laboratory. Cutting-edge and authoritative, Lateralized Brain Function: Methods in Human and Non-Human Species, Second Edition is a valuable resource for further research on brain and behavioral asymmetries in human and non-human species. This book will provide both expert and novel researchers with answers to their questions, while also encouraging vital collaborations.

EEG-Based Diagnosis of Alzheimer Disease

EEG-Based Diagnosis of Alzheimer Disease: A Review and Novel Approaches for Feature Extraction and Classification Techniques provides a practical and easy-to-use guide for researchers in EEG signal processing techniques, Alzheimer's disease, and dementia diagnostics. The book examines different features of EEG signals used to properly diagnose Alzheimer's Disease early, presenting new and innovative results in the extraction and classification of Alzheimer's Disease using EEG signals. This book brings together the use of different EEG features, such as linear and nonlinear features, which play a significant role in diagnosing Alzheimer's Disease. - Includes the mathematical models and rigorous analysis of various classifiers and machine learning algorithms from a perspective of clinical deployment - Covers the history of EEG signals and their measurement and recording, along with their uses in clinical diagnostics - Analyzes spectral, wavelet, complexity and other features of early and efficient Alzheimer's Disease diagnostics - Explores support vector machine-based classification to increase accuracy