Signal processing for neuroscientists
Subtitle: a companion volume : advanced topics, nonlinear techniques and multi-channel analysis
By: Wim van Drongelen
Contributors: ScienceDirect (Online service)
Publisher: Elsevier
Published: 2010
Place of publication: Oxford
Language: eng
Format: [electronic resource] :
ISBN-13: 9780123849151
Subjects: Neurosciences; Neurology; Signal processing; Data processing; Digital techniques; Mathematical models
Description: The popularity of signal processing in neuroscience is increasing and with the current availability and development of computer hardware and software it is anticipated that the current growth will continue. Because electrode fabrication has improved and measurement equipment is getting less expensive, electrophysiological measurements with large numbers of channels are now very common. In addition, neuroscience has entered the age of light and fluorescence measurements are fully integrated into the researcher's toolkit. Because each image in a movie contains multiple pixels, these measurements are multi-channel by nature. Furthermore, the availability of both generic and specialized software packages for data analysis has altered the neuroscientist's attitude towards some of the more complex analysis techniques. This book is a companion to the previously published book, 'Signal Processing for Neuroscientists: An Introduction to the Analysis of Physiological Signals', which introduced readers to the basic concepts. It discusses several advanced techniques, rediscovers methods to describe nonlinear systems, and examines the analysis of mulit-channel recordings.
Notes: Includes bibliographical references.

Electronic reproduction. Amsterdam : Elsevier Science & Technology, 2010. Mode of access: World Wide Web. System requirements: Web browser. Title from title screen (viewed on Sep. 27, 2010). Access may be restricted to users at subscribing institutions.
Table of contents: title: Lomb's Algorithm and the Hilbert Transform; title: Modeling; title: Volterra Series; title: Wiener Series; title: Poisson-Wiener Series; title: Decomposition of Multi-Channel Data; title: Causality.