BMES1100 Biosignal processing in sport and health science (3 op)

This course provides an intuitive and practical introduction into basics of digital biosignal processing techniques and their applications in the field of sport and health science without an in-depth mathematical treatment. Topics include overview of biosignals, removal of artifacts, event detection, time- and frequency-domain analyses as well as basics of multivariate analyses and signal classification. To the extent possible, each topic is introduced with the minimum amount of information required to use and understand the approach along with enough information to apply the method in an intelligent manner. Thus, the course is designed to develop skills in the effective and appropriate application of signal processing methods, but it does not provide the skills necessary to develop new techniques and algorithms. The methods will be covered on the basis of real-life application examples in the field of sport and health science as much as possible. Python programming language is used throughout to apply the theory and techniques discussed to biosignals. The course also introduces basics of Python programming.

After completing this course successfully, the student can:

1. Describe special characteristics of biosignals

2. Explain underlying principles of basic biosignal processing methods

3. Select appropriate methods for solving real-life problems related to biosignal analysis

4. Describe basic principles of some advanced biosignal processing methods and recognize the problems that can be solved with them

5. Solve small-scale problems related to biosignal analysis using basic biosignal processing methods

6. Implement small-scale programs for biosignal analysis using Python programming language 

Complementation methods

· Active participation in contact sessions (each of the contact sessions consists of a lecture or short lectures, discussions, demonstratios and exercises).

· Home exercises