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Filtering
Goal: Learn the basics of Filtering
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).
- Q&A Session
- What is an impulse response, a step response and a frequency response of a filter?
- Do you know what the task of Low-Pass, High-Pass, Band-Pass and Band-Reject filters is?
- Practice session:
- Filtering (there is only one task, but quite long and very important one
)
- Report:
- Send the final version of the notebook (to download it, click in Google Colab:
File > Download > Download .ipynb). Make sure you have done the whole task!
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
Filtering
- Filters: When, Why, and How (Not) to Use Them – really important and useful review of filters!
- Filters: FIR vs IIR by Siemens
- Digital filter design for electrophysiological data – a practical approach (includes Open Access to publication)