Signal analysis 1100-2BF05
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
Type of course
Mode
Prerequisites (description)
Course coordinators
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
Additional information
Information on level of this course, year of study and semester when the course unit is delivered, types and amount of class hours - can be found in course structure diagrams of apropriate study programmes. This course is related to the following study programmes:
Additional information (registration calendar, class conductors, localization and schedules of classes), might be available in the USOSweb system: