Signal Processing, Dynamic System Modeling

Lecturers: Prof. Dr.-Ing. Bernhard U. Seeber
Prof. Dr.-Ing. Werner Hemmert
Prof. Reinhard Heckel
Prof. Dr.-Ing. Julijana Gjiorgieva
Practical course: Prof. Dr.-Ing. Bernhard U. Seeber
Clara Hollomey, PhD
Ali Saeedi
Semester: Summer semester
Target group: Obligatory course, Elite Master Program in Neuroengineering, MSNE
Course is only for MSNE students!
The lecture is held in English.
Breadth: 2/1/1 (Lecture/Exercise course/Practical training)
Exam: written, 90 min.
Time & Location:

Lecture (in English): Tuesday, 08:45 - 10:15 hours, N2128 N2128
Exercise course: Tuesday, 10:30 - 11:15 hours, N2128
Practical training: Thursday, 14:45 - 17:45 hours, N1135

Start: Lecture and Exercise course start on 23.04.2019
no lecture and exercise course on 21.05., 11.06. and 09.07.2019
Practical training from 25.04.2019;
appointments for practical training in moodle


This course introduces fundamental signal processing techniques applicable to a wide variety of neural and biomedical signals from different domains, e.g. inter- and intra-neuronal cell recordings, electroencephalography (EEG), electrocardiography (ECG), electromyography (EMG), as well as biologically inspired sensory data typical for the domains of multimodal interaction and robotics (such as audio and video data).

The course is devided into two parts, an introductory signal processing part and a dynamic system modeling part.

The signal processing part will include:

  • Correct sampling of continuous signals for offline as well as (blockwise) processing and analysis in real time
  • Properties of time- and frequency domain signal transformations: Laplace- and Fourier transform, z-transform,Discrete Fourier Transform, time-frequency uncertainty, effects of temporal windowing, Short-Term Fourier Transform
  • Properties of FIR and IIR filters and filter design. Minimum- and linear phase filters, phase and group delay
  • Time-frequency signal analysis including spectrograms
  • Filter banks

Examples will be given from neuronal and audio signals.

In the second part, the course addresses the modelling of dynamic systems based on pre-processed sensory data. This comprises the identification and evaluation of model structure and parameters describing the dynamical properties of the biological system to be modelled. Concepts and tools from information theory are using to quantify the ability of a biological dynamical system to process sensory information.

This course will link to control theory, controller design and information theory to illustrate the connection between biology and engineering approaches in line with dynamical systems modelling.