Signal Processing for Neuroscientists: An Introduction to the Analysis of Physiological Signals

by Wim van Drongelen

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 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®.

Genres: Neuroscience, Academic

320 pages, Hardcover

First published January 1, 2006

Reviews

[David · Rating 1 out of 5]

Close to useless. Too much emphasis on derivations, and very little on practical implementation (which the books bills itself as being geared towards). Several chapters on the mathematics of the Fourier transform, exactly one brief example with real data. I feel like a book, with code, purporting to be a useful guide to the actual implementation of these analysis methods, should get you to a point where you can do something as basic as "quantify the difference in alpha band power between these two signals" or "plot an interpretable power spectrum of the following EEG signal".

So I'm not really sure what the point of this book is supposed to be - if you want a thorough treatment of the relevant mathematics there are better and more comprehensive texts, and if you want a guide to practical implementation then you'll be disappointed. Look at "Analyzing Neural Time Series Data" instead.