Inverse Problems in Bioelectromagnetism - Interactive curriculae of TU Ilmenau
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| module properties module number 201261 - common information | |
|---|---|
| module number | 201261 |
| department | Department of Computer Science and Automation |
| ID of group | 2221 (Biomedical Engineering) |
| module leader | Prof. Dr. Jens Haueisen |
| language | Englisch |
| term | Wintersemester |
| previous knowledge and experience | Anatomy, physiology, and basic clinical knowledge of the Biomedical Engineering (BSc) program, Medical Instrumentation. |
| learning outcome | Students know and understand the fundamentals of the optimization methods used, and are able to evaluate and apply them. The students are able to recognize and analyze inverse problems in biomedical engineering. Students are able to design a solution strategy for given inverse problems and implement it, the latter being practiced in the exercises and lab work. Students are able to communicate clearly and correctly on optimization and inverse problems in biomedical engineering. Through the lectures, students are aware of the multi-faceted approaches to inverse problems in bioelectromagnetism and are able to consider them in the application-related topics in the practical exercises. With the knowledge acquired in the lecture, students are able to participate with interest in the topic-specific discussions during the exercises. They are thus able to actively participate in the scientific discourse and are prepared to answer questions directed at them. The lectures and exercises provide the ability to accept and acknowledge different views on inverse problems in bioelectromagnetism. Based on the practical activities, students are able to plan and develop appropriate solutions to inverse bioelectromagnetics problems. Students are able to represent systems expertise for inverse bioelectromagnetic problems in interdisciplinary teams. |
| content | Introduction (motivation, definition and classification of inverse problems in biomedical engineering, differentiation from imaging techniques, definitions of terms, repetition of metrological constraints, forward models, source models) Deterministic and stochastic optimization methods (Deterministic: gradient-free and gradient-based methods, Stochastic: evolutionary algorithms, simulated annealing, particle swarm optimization) Advanced source models (neurobiological foundations, neural mass models, neural field models). A priori information and regularization techniques (incorporation of anatomical and neurobiological information, optimal regularization parameters). Bioelectromagentic source reconstruction (spatio-temporal dipole analysis, minimum-norm methods). Scanning methods (spatial filters, beamformers, multiple signal classification) Data fusion techniques of different modalities (EEG / MEG / fMRI / PET); predictive models. Three lab courses: Multichannel EEG derivation EEG source localization SEP/SEF reconstruction |
| media of instruction and technical requirements for education and examination in case of online participation | White board, lecture notes, slides, computer-based presentations, demonstrations, exercise tasks, lab setups, software |
| literature / references | Knösche & Haueisen: EEG/MEG source reconstruction. Springer 2022 Fletcher, R.: Practical methods of optimization. J W & S, Chichester, 1987 Bäck, T. und Schwefel, H.-P.: Evolutionary algorithms in theory and practice: Evolution strategies, evolutionary programming, genetic algorithms. Oxford University Press, NY, 1996 Louis, A.K.: Inverse und schlecht gestellte Probleme. Teubner 1989. Haueisen, J.: Numerische Berechnung und Analyse biomagnetischer Felder. Wissenschaftsverlag Ilmenau, 2004 Wilfried Andrä, Hannes Nowak (Editors): Magnetism in Medicine: A Handbook, 2nd, Completely Revised and Enlarged Edition, Wiley, 2006 Sarvas J, Ilmoniemi RJ: Brain Signals: Physics and Mathematics of MEG and EEG. MIT Press, 2019 |
| evaluation of teaching | |
| Details reference subject | |
|---|---|
| module name | Inverse Problems in Bioelectromagnetism |
| examination number | 220500 |
| credit points | 5 |
| SWS | 2.75 (1 V, 1 Ü, 75 P) |
| on-campus program (h) | 30.9375 |
| self-study (h) | 119.0625 |
| obligation | obligatory module |
| exam | examination performance with multiple performances |
| details of the certificate | Das Modul Inverse Problems in Bioelectromagnetism mit der Prüfungsnummer 220500 schließt mit folgenden Leistungen ab:
A documented instruction is required each semester in order to carry out laboratory experiments. |
| link to Moodle course | |
| teacher | Prof. Haueisen, Prof. Knösche, Dr. Petkovic |
| signup details for alternative examinations | |
| maximum number of participants | |
| Details in degree program Master Biomedical Engineering by Research 2026 | |
|---|---|
| module name | Inverse Problems in Bioelectromagnetism |
| examination number | 220500 |
| credit points | 5 |
| on-campus program (h) | 31 |
| self-study (h) | 119 |
| obligation | elective module |
| exam | examination performance with multiple performances |
| details of the certificate | Das Modul Inverse Problems in Bioelectromagnetism mit der Prüfungsnummer 220500 schließt mit folgenden Leistungen ab:
A documented instruction is required each semester in order to carry out laboratory experiments. |
| link to Moodle course | |
| signup details for alternative examinations | |
| maximum number of participants | |
| Details in degree program Master Biomedical Engineering by Research 2025 | |
|---|---|
| module name | Inverse Problems in Bioelectromagnetism |
| examination number | 220500 |
| credit points | 5 |
| on-campus program (h) | 22 |
| self-study (h) | 128 |
| obligation | elective module |
| exam | examination performance with multiple performances |
| details of the certificate | Das Modul Inverse Problems in Bioelectromagnetism mit der Prüfungsnummer 220500 schließt mit folgenden Leistungen ab:
A documented instruction is required each semester in order to carry out laboratory experiments. |
| link to Moodle course | |
| signup details for alternative examinations | |
| maximum number of participants | |