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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.
This book examines theoretical and applied aspects of wavelet
analysis in neurophysics, describing in detail different practical
applications of the wavelet theory in the areas of neurodynamics
and neurophysiology and providing a review of fundamental work that
has been carried out in these fields over the last decade. Chapters
1 and 2 introduce and review the relevant foundations of
neurophysics and wavelet theory, respectively, pointing on one hand
to the various current challenges in neuroscience and introducing
on the other the mathematical techniques of the wavelet transform
in its two variants (discrete and continuous) as a powerful and
versatile tool for investigating the relevant neuronal dynamics.
Chapter 3 then analyzes results from examining individual neuron
dynamics and intracellular processes. The principles for
recognizing neuronal spikes from extracellular recordings and the
advantages of using wavelets to address these issues are described
and combined with approaches based on wavelet neural networks
(chapter 4). The features of time-frequency organization of EEG
signals are then extensively discussed, from theory to practical
applications (chapters 5 and 6). Lastly, the technical details of
automatic diagnostics and processing of EEG signals using wavelets
are examined (chapter 7). The book will be a useful resource for
neurophysiologists and physicists familiar with nonlinear dynamical
systems and data processing, as well as for graduat e students
specializing in the corresponding areas.
This book examines theoretical and applied aspects of wavelet
analysis in neurophysics, describing in detail different practical
applications of the wavelet theory in the areas of neurodynamics
and neurophysiology and providing a review of fundamental work that
has been carried out in these fields over the last decade. Chapters
1 and 2 introduce and review the relevant foundations of
neurophysics and wavelet theory, respectively, pointing on one hand
to the various current challenges in neuroscience and introducing
on the other the mathematical techniques of the wavelet transform
in its two variants (discrete and continuous) as a powerful and
versatile tool for investigating the relevant neuronal dynamics.
Chapter 3 then analyzes results from examining individual neuron
dynamics and intracellular processes. The principles for
recognizing neuronal spikes from extracellular recordings and the
advantages of using wavelets to address these issues are described
and combined with approaches based on wavelet neural networks
(chapter 4). The features of time-frequency organization of EEG
signals are then extensively discussed, from theory to practical
applications (chapters 5 and 6). Lastly, the technical details of
automatic diagnostics and processing of EEG signals using wavelets
are examined (chapter 7). The book will be a useful resource for
neurophysiologists and physicists familiar with nonlinear dynamical
systems and data processing, as well as for graduat e students
specializing in the corresponding areas.
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.
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