LIMITED OFFER
Save 50% on book bundles
Immediately download your ebook while waiting for your print delivery. No promo code needed.
Signal Processing for Neuroscientists introduces analysis techniques primarily aimed at neuroscientists and biomedical engineering students with a reasonable but modest backgr… Read more
LIMITED OFFER
Immediately download your ebook while waiting for your print delivery. No promo code needed.
Dedication
Preface
Chapter 1: Introduction
1.1 OVERVIEW
1.2 BIOMEDICAL SIGNALS
1.3 BIOPOTENTIALS
1.4 EXAMPLES OF BIOMEDICAL SIGNALS
1.5 ANALOG-TO-DIGITAL CONVERSION
1.6 MOVING SIGNALS INTO THE MATLAB ANALYSIS ENVIRONMENT
APPENDIX 1.1
Chapter 2: Data Acquisition
2.1 RATIONALE
2.2 THE MEASUREMENT CHAIN
2.3 SAMPLING AND NYQUIST FREQUENCY IN THE FREQUENCY DOMAIN
2.4 THE MOVE TO THE DIGITAL DOMAIN
Appendix 2.1
Chapter 3: Noise
3.1 INTRODUCTION
3.2 NOISE STATISTICS
3.3 SIGNAL-TO-NOISE RATIO
3.4 NOISE SOURCES
APPENDIX 3.1
APPENDIX 3.2
APPENDIX 3.3
APPENDIX 3.4
Chapter 4: Signal Averaging
4.1 INTRODUCTION
4.2 TIME LOCKED SIGNALS
4.3 SIGNAL AVERAGING AND RANDOM NOISE
4.4 NOISE ESTIMATES AND THE ± AVERAGE
4.5 SIGNAL AVERAGING AND NONRANDOM NOISE
4.6 NOISE AS A FRIEND OF THE SIGNAL AVERAGER
4.7 EVOKED POTENTIALS
4.8 OVERVIEW OF COMMONLY APPLIED TIME DOMAIN ANALYSIS TECHNIQUES
Chapter 5: Real and Complex Fourier Series
5.1 INTRODUCTION
5.2 THE FOURIER SERIES
5.3 THE COMPLEX FOURIER SERIES
5.4 EXAMPLES
APPENDIX 5.1
APPENDIX 5.2
Chapter 6: Continuous, Discrete, and Fast Fourier Transform
6.1 INTRODUCTION
6.2 THE FOURIER TRANSFORM
6.3 DISCRETE FOURIER TRANSFORM AND THE FFT ALGORITHM
6.4 UNEVENLY SAMPLED DATA
Chapter 7: Fourier Transform Applications
7.1 SPECTRAL ANALYSIS
7.2 TOMOGRAPHY
APPENDIX 7.1
Chapter 8: LTI Systems, Convolution, Correlation, and Coherence
8.1 INTRODUCTION
8.2 LINEAR TIME INVARIANT (LTI) SYSTEM
8.3 CONVOLUTION
8.4 AUTOCORRELATION AND CROSS-CORRELATION
8.5 COHERENCE
APPENDIX 8.1
Chapter 9: Laplace and z-Transform
9.1 INTRODUCTION
9.2 THE USE OF TRANSFORMS TO SOLVE ODEs
9.3 THE LAPLACE TRANSFORM
9.4 EXAMPLES OF THE LAPLACE TRANSFORM
9.5 THE Z-TRANSFORM
9.6 THE Z-TRANSFORM AND ITS INVERSE
9.7 EXAMPLE OF THE z-TRANSFORM
APPENDIX 9.1
APPENDIX 9.2
APPENDIX 9.3
Chapter 10: Introduction to Filters: The RC Circuit
10.1 INTRODUCTION
10.2 FILTER TYPES AND THEIR FREQUENCY DOMAIN CHARACTERISTICS
10.3 RECIPE FOR AN EXPERIMENT WITH AN RC CIRCUIT
Chapter 11: Filters: Analysis
11.1 INTRODUCTION
11.2 THE RC CIRCUIT
11.3 THE EXPERIMENTAL DATA
APPENDIX 11.1
APPENDIX 11.2
APPENDIX 11.3
Chapter 12: Filters: Specification, Bode Plot, and Nyquist Plot
12.1 INTRODUCTION: FILTERS AS LINEAR TIME INVARIANT (LTI) SYSTEMS
12.2 TIME DOMAIN RESPONSE
12.3 THE FREQUENCY CHARACTERISTIC
12.4 NOISE AND THE FILTER FREQUENCY RESPONSE
Chapter 13: Filters: Digital Filters
13.1 INTRODUCTION
13.2 IIR AND FIR DIGITAL FILTERS
13.3 AR, MA, AND ARMA FILTERS
13.4 FREQUENCY CHARACTERISTIC OF DIGITAL FILTERS
13.5 MATLAB IMPLEMENTATION
13.6 FILTER TYPES
13.7 FILTER BANK
13.8 FILTERS IN THE SPATIAL DOMAIN
APPENDIX 13.1
Chapter 14: Spike Train Analysis
14.1 INTRODUCTION
14.2 POISSON PROCESSES AND POISSON DISTRIBUTIONS
14.3 ENTROPY AND INFORMATION
14.4 THE AUTOCORRELATION FUNCTION
14.5 CROSS-CORRELATION
APPENDIX 14.1
APPENDIX 14.2
Chapter 15: Wavelet Analysis: Time Domain Properties
15.1 INTRODUCTION
15.2 WAVELET TRANSFORM
15.3 OTHER WAVELET FUNCTIONS
15.4 TWO-DIMENSIONAL APPLICATION
APPENDIX 15.1
Chapter 16: Wavelet Analysis: Frequency Domain Properties
16.1 INTRODUCTION
16.2 THE CONTINUOUS WAVELET TRANSFORM (CWT)
16.3 TIME FREQUENCY RESOLUTION
16.4 MATLAB WAVELET EXAMPLES
Chapter 17: Nonlinear Techniques
17.1 INTRODUCTION
17.2 NONLINEAR DETERMINISTIC PROCESSES
17.3 LINEAR TECHNIQUES FAIL TO DESCRIBE NONLINEAR DYNAMICS
17.4 EMBEDDING
17.5 METRICS FOR CHARACTERIZING NONLINEAR PROCESSES
17.6 APPLICATION TO BRAIN ELECTRICAL ACTIVITY
References
Index
Wv
He worked for the Netherlands Organization for the Advancement of Pure Research (ZWO) in the Department of Animal Physiology, Wageningen, The Netherlands. He lectured and founded a Medical Technology Department at the HBO Institute Twente, The Netherlands. In 1986 he joined the Benelux office of Nicolet Biomedical as an Application Specialist and in 1993 he relocated to Madison, WI, USA where he was involved in research and development of equipment for clinical neurophysiology and neuromonitoring.
In 2001 he joined the Epilepsy Center at The University of Chicago, Chicago, IL, USA. Currently he is Professor of Pediatrics, Neurology, and Computational Neuroscience. In addition to his faculty position he serves as Technical and Research Director of the Pediatric Epilepsy Center and he is Senior Fellow with the Computation Institute. Since 2003 he teaches applied mathematics courses for the Committee on Computational Neuroscience. His ongoing research interests include the application of signal processing and modeling techniques to help resolve problems in neurophysiology and neuropathology.
For details of recent work see http://epilepsylab.uchicago.edu/