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Lecturer: Dr Juhász Zoltán

The purpose of the course is to provide an overview of the most important signal processing procedures used in bioelectrical measurements, with particular regard to ECG and EEG signal processing, as well as their practical applications.


Overview of the sources and properties of biomedical signals from the signal processing point of view: the theory of one- and two-dimensional signals.

Filtering tasks in biomedical signal processing, types of digital filters, the effect of their application on the measured signals.

Averaging finite-time signal segments. Mean value estimation for continuous and discrete signals. Variance and correlation estimates. Synchronized averaging for statistically independent signals and its application for computing EEG evoked potentials (Event Related Potentials).

Frequency domain techniques. Fourier transformation in single- and multivariate cases, short-time Fourier transformation, time-frequency representations, Wavelet transformations (Gábor, Morlet, etc. wavelets). Power density spectra, cross-spectral density, coherence functions. Auto- and cross-correlation functions, their multivariate counterparts. Theoretical issues of non-stationary signal processing.

Theory and application of Independent Component Analysis (ICA) during ECG and EEG processing and potential mapping.

Time series analysis, linear prediction. Autoregressive (AR) models. Estimation of AR parameters. Moving average (MA), mixed autoregressive moving average (ARMA). Estimation of parameters of ARMA models. Calculation of connectivity networks based on phase synchronization.


1.       Cohen, A.: Biomedical Signal Processing. CRC Press Inc., Boca Raton FL., 1987.

2.       Oppenheim, A.V., Schafer, R.W.: Digital Signal Processing. Prentice-Hall Inc., Englewood Cliffs, NJ, 1975.

3.       Tompkins, W.J. ed.: Biomedical Digital Signal Processing. Prentice-Hall Inc., Englewood Cliffs, NJ, 1993.

4.       Akay, M: Time Frequency and Wavelets in Biomedical Signal Processing. IEEE Inc., New York, 1996.

5.       Mike X Cohen, Analyzing Neural Time Series Data: Theory and Practice, The MIT Press, 2014.