Signal Processing for Neuroscientists, A Companion Volume

Signal Processing for Neuroscientists, A Companion Volume, 1st Edition

Advanced Topics, Nonlinear Techniques and Multi-Channel Analysis

Signal Processing for Neuroscientists, A Companion Volume, 1st Edition,Wim van Drongelen,ISBN9780123849151






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A step-by-step guide to signal processing

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Key Features

  • Covers the more advanced topics of linear and nonlinear systems analysis and multi-channel analysis
  • Includes practical examples implemented in MATLAB
  • Provides multiple references to the basics to help the student


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 toward some of the more complex analysis techniques.

This book is a companion to the previously published 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 multi-channel recordings.


Neuroscientists and biomedical engineering students.

Wim van Drongelen

Affiliations and Expertise

University of Chicago, Department of Pediatrics, Illinois, USA

Signal Processing for Neuroscientists, A Companion Volume, 1st Edition

 1. Lomb’s Algorithm and the Hilbert Transform

2. Modeling

3. Volterra Series

4. Wiener Series

5. Poisson-Wiener Series

6. Decomposition of Multi-Channel Data

7. Causality



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