Signal Processing for Neuroscientists: An Introduction to the Analysis of Physiological Signals
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®.
* Multiple color illustrations are integrated in the text
* Includes an introduction to biomedical signals, noise characteristics, and recording techniques
* Basics and background for more advanced topics can be found in extensive notes and appendices
* A Companion Website hosts the MATLAB scripts and several data files:
http://www.elsevierdirect.com/companion.jsp?ISBN=9780123708670
Why Read This Book
You will get a practical, approachable introduction to the core signal-processing techniques used on physiological data, taught with neuroscientists in mind rather than pure mathematicians. You will learn how averaging, Fourier analysis, filtering, wavelets and coherence are applied end-to-end — from data acquisition to real-world analysis — with an informal mathematical style that emphasizes intuition and hands-on use.
Who Will Benefit
Neuroscientists, biomedical engineers, and graduate students with modest mathematical and programming background who need to analyze EEG/MEG, LFPs, single-unit, or other physiological signals.
Level: Intermediate — Prerequisites: Basic calculus and linear algebra, elementary probability/statistics, and familiarity with a scientific programming environment (MATLAB or Python recommended).
Key Takeaways
- Implement time-domain techniques such as averaging, convolution, and cross-correlation to extract evoked responses and temporal relationships.
- Apply Fourier methods and the FFT to compute spectra, spectrograms, and basic spectral estimation for physiological signals.
- Design and use digital filters (FIR and IIR) appropriate for biomedical data while understanding practical issues like edge effects and phase distortion.
- Use wavelet and time–frequency transforms to detect and characterize transient, nonstationary events in neurophysiological recordings.
- Compute coherence and cross-spectral measures to quantify functional connectivity and synchrony between channels.
- Apply basic statistical signal-processing ideas and cleaning methods (including adaptive filtering and artifact rejection) to produce reproducible results.
Topics Covered
- 1. Introduction to Signals, Noise, and Data Acquisition
- 2. Time-Domain Analysis: Averaging, Convolution, and Correlation
- 3. Linear Systems and Filtering: FIR, IIR and Practical Considerations
- 4. Fourier Analysis and the Fast Fourier Transform (FFT)
- 5. Spectral Estimation, Windowing, and Leakage
- 6. Cross-Spectra, Coherence and Phase Analysis
- 7. Wavelet Transforms and Time–Frequency Methods
- 8. Adaptive Filtering and Artifact Removal
- 9. Statistical Considerations: Detection, Significance and Variability
- 10. Practical Data-Processing Pipelines for EEG/MEG/LFP
- 11. Implementations, Examples and Software Tools
- Appendices: Mathematical Background and Reference Tables
Languages, Platforms & Tools
How It Compares
More neuroscience-focused and accessible than Steven W. Smith's general DSP texts, and less clinically extensive than Rangayyan's or other biomedical-signal-specific books — it sits between practical DSP introductions and specialized biomedical signal-analysis references.
