Analyzing Neural Time Series Data: Theory and Practice (Issues in Clinical and Cognitive Neuropsychology) Kindle Edition
Author: Visit ‘s Mike X Cohen Page ID: B00HZDIY26
Done.
File Size: 11838 KBPrint Length: 600 pagesPublisher: The MIT Press; 1 edition (January 17, 2014)Publication Date: January 17, 2014 Sold by: Digital Services, Inc. Language: EnglishID: B00HZDIY26Text-to-Speech: Enabled X-Ray: Not Enabled Word Wise: Not EnabledLending: Enabled Enhanced Typesetting: Not Enabled Best Sellers Rank: #256,995 Paid in Kindle Store (See Top 100 Paid in Kindle Store) #18 in Kindle Store > Kindle eBooks > Medical eBooks > Specialties > Radiology > Diagnostic Imaging #115 in Books > Medical Books > Medicine > Internal Medicine > Radiology > Diagnostic Imaging #193 in Kindle Store > Kindle eBooks > Medical eBooks > Internal Medicine > Neurology > Neuroscience
This is a wonderful book. It covers not only the mathematical aspects of EEG signal processing but also the physiological bases of it, and the author makes this using a language which is understandable by non-mathematicians and it is also precise and clear.
I am a mathematician starting to work on the topic and I am completely sure that this work will increase and reinforce both my understanding of this subject, and my increIDg love for it! I strongly recommend this book to everybody interested on EEGs. I just would like to add that this book has a very good complement in another one, published by the very same author and entitled "Fundamentals of Time-Frequency Analyses in Matlab/Octave" (Sinc press, 2014, there exists only Kindle version of it).
I have had my training as a EE major specializing in signal processing and moved on to do experimental work in neuroscience. Having said that I feel confident in both signal processing and electrophysiology. Mike Cohen has done a great job collecting almost all the useful techniques needed for different types of neural signal processing. An amazing feature of the book as promised on the cover is how Mike has managed to masterfully merge theory and practice without losing accuracy in either. The book, in my opinion, is a must have on the bookshelf of every investigator/student who deals with one sort of neural data.
I’m a grad student and this book is absolutely perfect for someone in my position. Many of the issues I’ve come across when looking at neurological data are covered here with great explanations at how to solve them and why these solutions are used. It’s fairly easy to read, I feel like it is concise without being imprecise and has plenty of useful illustrations. I appreciate that it is written with actual application of the techniques kept in mind, it feels like a very good guidebook to practical analysis. I wish I had picked up this book at the start of my grad school experience rather than the middle, as this would’ve made things much more smooth.
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