Front cover -- Signal Processing for Neuroscientists -- Copyright page -- Preface -- Table of contents -- 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.