Signal Processing For Neuroscientists PDF Download
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Author | : Wim van Drongelen |
Publisher | : Elsevier |
Total Pages | : 319 |
Release | : 2006-12-18 |
Genre | : Science |
ISBN | : 008046775X |
Download Signal Processing for Neuroscientists Book in PDF, ePub and Kindle
Signal Processing for Neuroscientists introduces analysis techniques primarily aimed at neuroscientists and biomedical engineering students with a reasonable but modest background in mathematics, physics, and computer programming. The focus of this text is on what can be considered the ‘golden trio’ in the signal processing field: averaging, Fourier analysis, and filtering. Techniques such as convolution, correlation, coherence, and wavelet analysis are considered in the context of time and frequency domain analysis. The whole spectrum of signal analysis is covered, ranging from data acquisition to data processing; and from the mathematical background of the analysis to the practical application of processing algorithms. Overall, the approach to the mathematics is informal with a focus on basic understanding of the methods and their interrelationships rather than detailed proofs or derivations. One of the principle goals is to provide the reader with the background required to understand the principles of commercially available analyses software, and to allow him/her to construct his/her own analysis tools in an environment such as MATLAB®. Multiple color illustrations are integrated in the text Includes an introduction to biomedical signals, noise characteristics, and recording techniques Basics and background for more advanced topics can be found in extensive notes and appendices A Companion Website hosts the MATLAB scripts and several data files: http://www.elsevierdirect.com/companion.jsp?ISBN=9780123708670
Author | : Wim van Drongelen |
Publisher | : Academic Press |
Total Pages | : 740 |
Release | : 2018-04-20 |
Genre | : Science |
ISBN | : 012810483X |
Download Signal Processing for Neuroscientists Book in PDF, ePub and Kindle
Signal Processing for Neuroscientists, Second Edition provides an introduction to signal processing and modeling for those with a modest understanding of algebra, trigonometry and calculus. With a robust modeling component, this book describes modeling from the fundamental level of differential equations all the way up to practical applications in neuronal modeling. It features nine new chapters and an exercise section developed by the author. Since the modeling of systems and signal analysis are closely related, integrated presentation of these topics using identical or similar mathematics presents a didactic advantage and a significant resource for neuroscientists with quantitative interest. Although each of the topics introduced could fill several volumes, this book provides a fundamental and uncluttered background for the non-specialist scientist or engineer to not only get applications started, but also evaluate more advanced literature on signal processing and modeling. Includes an introduction to biomedical signals, noise characteristics, recording techniques, and the more advanced topics of linear, nonlinear and multi-channel systems analysis Features new chapters on the fundamentals of modeling, application to neuronal modeling, Kalman filter, multi-taper power spectrum estimation, and practice exercises Contains the basics and background for more advanced topics in extensive notes and appendices Includes practical examples of algorithm development and implementation in MATLAB Features a companion website with MATLAB scripts, data files, figures and video lectures
Author | : Xiaoli Li |
Publisher | : Springer |
Total Pages | : 289 |
Release | : 2016-08-31 |
Genre | : Medical |
ISBN | : 9811018227 |
Download Signal Processing in Neuroscience Book in PDF, ePub and Kindle
This book reviews cutting-edge developments in neural signalling processing (NSP), systematically introducing readers to various models and methods in the context of NSP. Neuronal Signal Processing is a comparatively new field in computer sciences and neuroscience, and is rapidly establishing itself as an important tool, one that offers an ideal opportunity to forge stronger links between experimentalists and computer scientists. This new signal-processing tool can be used in conjunction with existing computational tools to analyse neural activity, which is monitored through different sensors such as spike trains, local filed potentials and EEG. The analysis of neural activity can yield vital insights into the function of the brain. This book highlights the contribution of signal processing in the area of computational neuroscience by providing a forum for researchers in this field to share their experiences to date.
Author | : Li Hu |
Publisher | : Springer Nature |
Total Pages | : 437 |
Release | : 2019-10-12 |
Genre | : Medical |
ISBN | : 9811391130 |
Download EEG Signal Processing and Feature Extraction Book in PDF, ePub and Kindle
This book presents the conceptual and mathematical basis and the implementation of both electroencephalogram (EEG) and EEG signal processing in a comprehensive, simple, and easy-to-understand manner. EEG records the electrical activity generated by the firing of neurons within human brain at the scalp. They are widely used in clinical neuroscience, psychology, and neural engineering, and a series of EEG signal-processing techniques have been developed. Intended for cognitive neuroscientists, psychologists and other interested readers, the book discusses a range of current mainstream EEG signal-processing and feature-extraction techniques in depth, and includes chapters on the principles and implementation strategies.
Author | : Pascal Wallisch |
Publisher | : Academic Press |
Total Pages | : 571 |
Release | : 2014-01-09 |
Genre | : Psychology |
ISBN | : 0123838371 |
Download MATLAB for Neuroscientists Book in PDF, ePub and Kindle
MATLAB for Neuroscientists serves as the only complete study manual and teaching resource for MATLAB, the globally accepted standard for scientific computing, in the neurosciences and psychology. This unique introduction can be used to learn the entire empirical and experimental process (including stimulus generation, experimental control, data collection, data analysis, modeling, and more), and the 2nd Edition continues to ensure that a wide variety of computational problems can be addressed in a single programming environment. This updated edition features additional material on the creation of visual stimuli, advanced psychophysics, analysis of LFP data, choice probabilities, synchrony, and advanced spectral analysis. Users at a variety of levels—advanced undergraduates, beginning graduate students, and researchers looking to modernize their skills—will learn to design and implement their own analytical tools, and gain the fluency required to meet the computational needs of neuroscience practitioners. The first complete volume on MATLAB focusing on neuroscience and psychology applications Problem-based approach with many examples from neuroscience and cognitive psychology using real data Illustrated in full color throughout Careful tutorial approach, by authors who are award-winning educators with strong teaching experience
Author | : Mike X Cohen |
Publisher | : MIT Press |
Total Pages | : 615 |
Release | : 2014-01-17 |
Genre | : Psychology |
ISBN | : 0262019876 |
Download Analyzing Neural Time Series Data Book in PDF, ePub and Kindle
A comprehensive guide to the conceptual, mathematical, and implementational aspects of analyzing electrical brain signals, including data from MEG, EEG, and LFP recordings. This book offers a comprehensive guide to the theory and practice of analyzing electrical brain signals. It explains the conceptual, mathematical, and implementational (via Matlab programming) aspects of time-, time-frequency- and synchronization-based analyses of magnetoencephalography (MEG), electroencephalography (EEG), and local field potential (LFP) recordings from humans and nonhuman animals. It is the only book on the topic that covers both the theoretical background and the implementation in language that can be understood by readers without extensive formal training in mathematics, including cognitive scientists, neuroscientists, and psychologists. Readers who go through the book chapter by chapter and implement the examples in Matlab will develop an understanding of why and how analyses are performed, how to interpret results, what the methodological issues are, and how to perform single-subject-level and group-level analyses. Researchers who are familiar with using automated programs to perform advanced analyses will learn what happens when they click the “analyze now” button. The book provides sample data and downloadable Matlab code. Each of the 38 chapters covers one analysis topic, and these topics progress from simple to advanced. Most chapters conclude with exercises that further develop the material covered in the chapter. Many of the methods presented (including convolution, the Fourier transform, and Euler's formula) are fundamental and form the groundwork for other advanced data analysis methods. Readers who master the methods in the book will be well prepared to learn other approaches.
Author | : Fabrizio Gabbiani |
Publisher | : Academic Press |
Total Pages | : 630 |
Release | : 2017-02-04 |
Genre | : Mathematics |
ISBN | : 0128019069 |
Download Mathematics for Neuroscientists Book in PDF, ePub and Kindle
Mathematics for Neuroscientists, Second Edition, presents a comprehensive introduction to mathematical and computational methods used in neuroscience to describe and model neural components of the brain from ion channels to single neurons, neural networks and their relation to behavior. The book contains more than 200 figures generated using Matlab code available to the student and scholar. Mathematical concepts are introduced hand in hand with neuroscience, emphasizing the connection between experimental results and theory. Fully revised material and corrected text Additional chapters on extracellular potentials, motion detection and neurovascular coupling Revised selection of exercises with solutions More than 200 Matlab scripts reproducing the figures as well as a selection of equivalent Python scripts
Author | : Suzanna Becker |
Publisher | : MIT Press |
Total Pages | : 1738 |
Release | : 2003 |
Genre | : Neural circuitry |
ISBN | : 9780262025508 |
Download Advances in Neural Information Processing Systems 15 Book in PDF, ePub and Kindle
Proceedings of the 2002 Neural Information Processing Systems Conference.
Author | : Lawrence K. Saul |
Publisher | : MIT Press |
Total Pages | : 1710 |
Release | : 2005 |
Genre | : Computational intelligence |
ISBN | : 9780262195348 |
Download Advances in Neural Information Processing Systems 17 Book in PDF, ePub and Kindle
Papers presented at NIPS, the flagship meeting on neural computation, held in December 2004 in Vancouver.The annual Neural Information Processing Systems (NIPS) conference is the flagship meeting on neural computation. It draws a diverse group of attendees--physicists, neuroscientists, mathematicians, statisticians, and computer scientists. The presentations are interdisciplinary, with contributions in algorithms, learning theory, cognitive science, neuroscience, brain imaging, vision, speech and signal processing, reinforcement learning and control, emerging technologies, and applications. Only twenty-five percent of the papers submitted are accepted for presentation at NIPS, so the quality is exceptionally high. This volume contains the papers presented at the December, 2004 conference, held in Vancouver.
Author | : Robert E. Kass |
Publisher | : Springer |
Total Pages | : 663 |
Release | : 2014-07-08 |
Genre | : Medical |
ISBN | : 1461496020 |
Download Analysis of Neural Data Book in PDF, ePub and Kindle
Continual improvements in data collection and processing have had a huge impact on brain research, producing data sets that are often large and complicated. By emphasizing a few fundamental principles, and a handful of ubiquitous techniques, Analysis of Neural Data provides a unified treatment of analytical methods that have become essential for contemporary researchers. Throughout the book ideas are illustrated with more than 100 examples drawn from the literature, ranging from electrophysiology, to neuroimaging, to behavior. By demonstrating the commonality among various statistical approaches the authors provide the crucial tools for gaining knowledge from diverse types of data. Aimed at experimentalists with only high-school level mathematics, as well as computationally-oriented neuroscientists who have limited familiarity with statistics, Analysis of Neural Data serves as both a self-contained introduction and a reference work.