Technische Universität Ilmenau

Specific Methods for Biosignal Processing - Interactive curriculae of TU Ilmenau

The interactive curriculae provide information on the degree programmes offered by the TU Ilmenau.

Please refer to the respective study and examination rules and regulations for the legally binding curricula (Annex Curriculum).

You can find all details on planned lectures and classes in the course catalogue.

Please note that this page is no longer updated. All modules and study plans from PO version 2021 onwards (Bachelor and Master study programs) are now available on the Campus Portal.

module properties Specific Methods for Biosignal Processing in degree program Master Biomedical Engineering by Research 2025
module number201269
examination number2200894
departmentDepartment of Computer Science and Automation
ID of group 2225 (Data Processing in the Life Sciences)
module leaderProf. Dr. Patrique Fiedler
term winter term only
languageEnglisch
credit points5
on-campus program (h)34
self-study (h)116
obligationelective module
examoral examination performance, 30 minutes
details of the certificate
link to Moodle course
teacherProf. Fiedler
signup details for alternative examinations
maximum number of participants
previous knowledge and experience
  • Signal and systems theory
  • Math
  • Basics in anatomy and physiology
  • Basics in electro- and neurophysiology
  • Signal Processing for Biomedical Engineering
  • Biostatistcs
  • Process measurement and sensor technology
learning outcomeProfessional competences: The students know essential details of the relevant selected methods with regard to the signal properties and the results, especially with regard to diagnostic or therapeutic goals. The students know the advantages compared to the established standard in the clinic.

Methodological competences: The students are able to methodically analyze biosignal processing, especially in practice, to a level that goes significantly beyond the usual standard, to process the biosignals and, based on this, to provide a reliable basis for diagnostics.

Social skills: Through discussions in the lecture and in the exercise, the students gain a realistic and critical view on the specific procedures through which they can demonstrate a competence advantage in clinical research and routine. The students can appreciate the opinions of other fellow students.
content
  • Independent Component Analysis
  • Matching Pursuit
  • Tensor based data decomposition
  • Higher-Order Spectra Analysis
  • State models
  • Multipole based data decomposition
  • Compressed Sensing
  • Spherical Harmonics Analysis
media of instruction and technical requirements for education and examination in case of online participationSlides with projector for the lecture, blackboard, computer simulations. Whiteboard and computing cabinet for the seminar.
literature / references
  1. Bronzino, J. D. (Ed.): The Biomedical Engineering Handbook, Vol. I + II, 2nd ed., CRC Press, Boca Raton 2000
  2. Husar, P.: Electrical Biosignals in Biomedical Engineering, Springer, 2023, 1st edition.
  3. Bendat J., Piersol A.: Measurement and Analysis of Random Data. John Wiley, 1986.
  4. Proakis, J.G, Manolakis, D.G.: Digital Signal Processing, Pearson Prentice Hall, 2007
  5. Durka, P: Matching Pursuit and Unification in EEG Analysis. Artech House Inc; April 2007
  6. Nikias, C.L., Petropolu, A.P.: Higher-Order Spectra Analysis. PTR Prentice-Hall Inc., 1993
  7. Hyvärinen, A., Karhunen, J., Oja, E.: Independent Component Analysis, John Wiley & Sons, 2001
  8. Graichen et al.: SPHARA - A Generalized Spatial Fourier Analysis for Multi-Sensor Systems with Non-Uniformly Arranged Sensors: Application to EEG, PLoS One, 2015
evaluation of teaching