Hramov / Koronovskii / Makarov Wavelets in Neuroscience

2. Auflage 2021
ISBN: 978-3-030-75992-6
Verlag: Springer International Publishing
Format: PDF
Kopierschutz: 1 - PDF Watermark

E-Book, Englisch, 384 Seiten, eBook

Reihe: Springer Series in Synergetics

This book illustrates how modern mathematical wavelet transform techniques offer fresh insights into the complex behavior of neural systems at different levels: from the microscopic dynamics of individual cells to the macroscopic behavior of large neural networks. It also demonstrates how and where wavelet-based mathematical tools can provide an advantage over classical approaches used in neuroscience. The authors well describe single neuron and populational neural recordings.This 2nd edition discusses novel areas and significant advances resulting from experimental techniques and computational approaches developed since 2015, and includes three new topics:
• Detection of fEPSPs in multielectrode LFPs recordings.

• Analysis of Visual Sensory Processing in the Brain and BCI for Human Attention Control;
• Analysis and Real-time Classification of Motor-related EEG Patterns;The book is a valuable resource for neurophysiologists and physicists familiar with nonlinear dynamical systems and data processing, as well as for graduate students specializing in these and related areas.

Zielgruppe

Research

Autoren/Hrsg.

Hramov, Alexander E.

Koronovskii, Alexey A.

Makarov, Valeri A.

Maksimenko, Vladimir A.

Pavlov, Alexey N.

Sitnikova, Evgenia

Inhaltsverzeichnis

Mathematical Methods of Signal Processing in Neuroscience.- Brief Tour of Wavelet Theory.- Analysis of Single Neuron Recordings.- Classification of Neuronal Spikes from Extracellular Recordings.- Analysis of Gamma-Waves in Multielectrode LFP Recordings.- Wavelet Approach to the Study of Rhythmic Neuronal Activity.- Wavelet-based Approach to Epilepsy.- Analysis of Visual Sensory Processing in the Brain and Brain-Computer Interfaces for Human Attention Control.- Analysis and Real-Time Classi?cation of Motor-related EEG and MEG Patterns.- Conclusion.