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Titlebook: Wavelets in Neuroscience; Alexander E. Hramov,Alexey A. Koronovskii,Evgenia Book 2021Latest edition The Editor(s) (if applicable) and The

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發(fā)表于 2025-3-21 18:43:00 | 只看該作者 |倒序?yàn)g覽 |閱讀模式
書(shū)目名稱Wavelets in Neuroscience
編輯Alexander E. Hramov,Alexey A. Koronovskii,Evgenia
視頻videohttp://file.papertrans.cn/1022/1021287/1021287.mp4
概述Illustrates how the wavelet transform provides new insights into the complex behavior of neural systems.Includes the latest topics relevant for a broad audience working in experimental and computation
叢書(shū)名稱Springer Series in Synergetics
圖書(shū)封面Titlebook: Wavelets in Neuroscience; Alexander E. Hramov,Alexey A. Koronovskii,Evgenia Book 2021Latest edition The Editor(s) (if applicable) and The
描述.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..
出版日期Book 2021Latest edition
關(guān)鍵詞Data Analysis in Neurophysics; Dynamical Systems in Neuroscience; Neurodynamics and Neurophysiology; Ne
版次2
doihttps://doi.org/10.1007/978-3-030-75992-6
isbn_softcover978-3-030-75994-0
isbn_ebook978-3-030-75992-6Series ISSN 0172-7389 Series E-ISSN 2198-333X
issn_series 0172-7389
copyrightThe Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerl
The information of publication is updating

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Wavelet-Based Diagnostics of Paroxysmal Activity in EEG and Brain-Computer Interfaces for Epilepsy s for automatic analysis of pre-recorded signals of neuronal activity (off-line diagnostics), and also some examples of wavelet-based EEG analysis in real-time (on-line). We also discuss principles of fast and precise detection of transient events in EEG and organization of close-loop control systems that can be used in brain-computer interface.
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發(fā)表于 2025-3-22 05:19:16 | 只看該作者
Analysis of Visual Sensory Processing in the Brain and Brain-Computer Interfaces for Human Attentiocept-related brain activity. Then, we considered the effect of the human condition (motivation and alertness) on the perceptive process. Finally, we review the basic principles of the brain-computer interfaces that use the wavelet-based algorithm to evaluate the human state in visual perception tasks.
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發(fā)表于 2025-3-22 11:40:32 | 只看該作者
Analysis of Visual Sensory Processing in the Brain and Brain-Computer Interfaces for Human Attentiocept-related brain activity. Then, we considered the effect of the human condition (motivation and alertness) on the perceptive process. Finally, we review the basic principles of the brain-computer interfaces that use the wavelet-based algorithm to evaluate the human state in visual perception tasks.
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發(fā)表于 2025-3-22 13:53:00 | 只看該作者
Mathematical Methods of Signal Processing in Neuroscience,sis in neuroscience, from the . (the dynamics of individual cells or intracellular processes) to the . (dynamics of large-scale neuronal networks in the brain as a whole, ascertained by analyzing electro- and magnetoencephalograms).
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Mathematical Methods of Signal Processing in Neuroscience,ive the rationale for the development of specific mathematical approaches for decoding information from non-stationary neurophysiological processes with time-varying features. Second, we focus on the development of mathematical methods for automatic processing and analysis of neurophysiological sign
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Brief Tour of Wavelet Theory,ition from Fourier analysis to wavelets. Mother functions and numerical techniques for implementing the wavelet transform are described. The problem of visualising the results is considered. Finally, features of the discrete wavelet transform are discussed.
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