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Psychophysiological measurement
Goal: Learn the basics of Digital Signal Processing (Analog-to-Digital conversion, Fourier transform)
Prepare for the lab
Materials
- Instead of a lecture, we go straight to the Q&A session with a short series of questions to “warm up” (based on the textbook). Also, in the hands-on session, the most important content is summarized alongside the practical activities.
- Q&A Session:
- What is sampling?
- What is quantization?
- What is aliasing?
- Do you know the sampling theorem? (a.k.a. Shannon sampling theorem / Nyquist sampling theorem)
- Practice session is placed in two notebooks:
Learn more!
Digital Signal Processing
- 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