Signal analysis 1100-2BF05

https://brain.fuw.edu.pl/edu/index.php/Analiza_sygnałów_-_wykład

Basic principles of the classical (spectral) and contemporary (wavelets, time-frequency) analysis of signals:
- Linear time invariant systems (LTI)
- Fourier series and Fourier transform
- Stochastic and autoregressive (AR) time series, power spectrum estimation. Convolution theorem.
- Sampling digital signals - Nyquist theorem, aliasing.
- Uncertainty principle in signal analysis, spectrogram, Wigner transform, wavelets, adaptive approximations (matching pursuit algorithm)
- briefly: multivariate signal analysis (PCA, ICA, MVAR)
- EEG, ERD/ERS, BCI

Course coordinators

Monika Szukałowicz

Type of course

obligatory courses

Mode

Classroom

Prerequisites (description)

Required credits from the following classes, conducted at the Faculty of Physics: 1. Information and Communication Technologies (Technologie informacyjne i komunikacyjne), with 45 hours of practical exercises in Python programming 2. Statistical Inference (Wnioskowanie Statystyczne)

Learning outcomes

having completed the course, student:
- understands the links between basic operations in time and frequency
- can choose a proper method of signal analysis and properly interpret the results

Assessment criteria

Written exam in the lecture hall: open questions and single choice test.

Positive marks from the lecture (as above) AND practical exercises are required to pass.

Practical placement

none

Bibliography

Skrypt "Analiza sygnałów" in Polish: https://brain.fuw.edu.pl/edu/index.php/Analiza_sygna%c5%82%c3%b3w_-_wyk%c5%82ad

Matching Pursuit and Unification in EEG Analysis https://us.artechhouse.com/Matching-Pursuit-and-Unification-in-EEG-Analysis-P539.aspx

Svarog software http://svarog.pl