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.