courses:psaw:lab_measurement [IIS Wiki]

Goal: Learn the basics of Digital Signal Processing (Analog-to-Digital conversion, Fourier transform)

Q&A Session:
1. What is sampling?
2. What is quantization?
3. What is aliasing?
4. Do you know the sampling theorem? (a.k.a. Shannon sampling theorem / Nyquist sampling theorem)
Practice session is placed in two notebooks:
1. ADC: Analog/Digital Conversion
2. Time-Frequency Analysis: Power Spectrum

Steven W. Smith - The Scientist and Engineer's Guide to Digital Signal Processing (California Technical Pub, 1997; full version available on-line)
Mike X Cohen - Analyzing Neural Time Series Data (The MIT Press, 2014) – probably the best book in DSP (it has “neural” in the title, but the concepts are more general)
- A set of video lectures (and Matlab scripts) by Mike X Cohen
- Code in Python by Lyndon Duong
Steven J. Luck - An Introduction to the Event-Related Potential Technique (Bradford Book, 2014) – the second important book in DSP; strongly aimed at EEG signals (a bible for ERP researchers), but also useful in other domains
Allen B. Downey - Think DSP: Digital Signal Processing in Python (source code) – Introduction to DSP with sound analysis examples
Marc Lichtman - PySDR: A Guide to SDR and DSP using Python – A set of materials aimed at wireless communication
Neuromatch Academy: Computational Neuroscience – aimed at neuro, but very useful for other domains

Psychophysiological measurement