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基本説明
Includes an introduction to biomedical signals, noise characteristics, and recording techniques.
Full Description
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 field: 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 (R).
Contents
Chapter 1Chapter 2: Data AcquisitionChapter 3: NoiseChapter 4: Signal AveragingChapter 5: Real and Complex Fourier SeriesChapter 6: Continuous, Discrete, and Fast Fourier TransformChapter 7: Fourier Transform ApplicationsChapter 8: LTI Systems, Convolution, Correlation, and CoherenceChapter 9: Laplace and z-TransformChapter 10: Introduction to Filters: The RC CircuitChapter 11: Filters: AnalysisChapter 12: Filters: Specification, Bode Plot, and Nyquist PlotChapter 13: Filters: Digital FiltersChapter 14: Spike Train AnalysisChapter 15: Wavelet Analysis: Time Domain PropertiesChapter 16: Wavelet Analysis: Frequency Domain PropertiesChapter 17: Nonlinear TechniquesReferencesIndex
