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NeuroInsight: Fundamental Areas of Neurotechnology

Module name (EN):
Name of module in study programme. It should be precise and clear.
NeuroInsight: Fundamental Areas of Neurotechnology
Degree programme:
Study Programme with validity of corresponding study regulations containing this module.
Biomedical Engineering, Bachelor, SO 01.10.2025
Module code: BMT3502.NIS
SAP-Submodule-No.:
The exam administration creates a SAP-Submodule-No for every exam type in every module. The SAP-Submodule-No is equal for the same module in different study programs.
P213-0246, P213-0247
Hours per semester week / Teaching method:
The count of hours per week is a combination of lecture (V for German Vorlesung), exercise (U for Übung), practice (P) oder project (PA). For example a course of the form 2V+2U has 2 hours of lecture and 2 hours of exercise per week.
4V+1P (5 hours per week)
ECTS credits:
European Credit Transfer System. Points for successful completion of a course. Each ECTS point represents a workload of 30 hours.
5
Semester: 5
Mandatory course: yes
Language of instruction:
German
Assessment:
KL (50%), PPA (50%)

[updated 21.04.2026]
Applicability / Curricular relevance:
All study programs (with year of the version of study regulations) containing the course.

BMT3502.NIS (P213-0246, P213-0247) Biomedical Engineering, Bachelor, SO 01.10.2025 , semester 5, mandatory course
Workload:
Workload of student for successfully completing the course. Each ECTS credit represents 30 working hours. These are the combined effort of face-to-face time, post-processing the subject of the lecture, exercises and preparation for the exam.

The total workload is distributed on the semester (01.04.-30.09. during the summer term, 01.10.-31.03. during the winter term).
75 class hours (= 56.25 clock hours) over a 15-week period.
The total student study time is 150 hours (equivalent to 5 ECTS credits).
There are therefore 93.75 hours available for class preparation and follow-up work and exam preparation.
Recommended prerequisites (modules):
None.
Recommended as prerequisite for:
Module coordinator:
Prof. Dr. Dr. Daniel Strauß
Lecturer: Prof. Dr. Dr. Daniel Strauß

[updated 07.08.2025]
Learning outcomes:
In this module students will gain systematic insight into key topics in neural engineering. After successfully completing this module, they will have a basic understanding of brain-computer interfaces, neural and cognitive systems, and neural signal processing and modeling. They will have basic knowledge of signal classes, signal transformations, and filter techniques, and will be proficient in fundamental methods for the practical processing of signals of neural origin in the context of neural engineering. In addition, they will be familiar with current research and development activities in the field of neural engineering.

[updated 21.04.2026]
Module content:
Fundamentals of the nervous system with a focus on information coding and functional organization,
Properties of neuroelectric signals and basic methods for their detection, representation, and processing
Introduction to neural and cognitive systems as information-processing structures, fundamentals of pattern recognition and AI: Feature extraction, simple classification principles, technical analogies to biological information processing processes, neural sources, functional patterns and their significance for modeling and neurotechnical applications, possibilities and limitations of neurotechnical measurement methods, and fundamental concepts of multimodal sensor integration, introduction to concepts of neural states and their functional role; Transfer of fundamental principles to AI-based systems, including “empathic AI.”
Overview of current research and development activities in the field of neural engineering


[updated 21.04.2026]
Recommended or required reading:
D. S. Levine "Introduction to Neural and Cognitive Modeling", Lawrence Erlbaum Associates, 2000
 
Jorge Rosner "Peeling the Onion: Gestalt Theory and Methodology", Gestalt-Institute of Toronto, 1990
 
J. R. Evans and A. Abarbanel "Introduction to Quantitative EEG and Neurofeedback", Academic Press, 1999
 
E. N. Bruce "Biomedical Signal Processing and Signal Modelling", John Wiley & Sons, 2001
 
P. L. Nunez, R. Shrinivasan Electric Fields of the Brain "The Neurophysics of EEG", Oxford University Press, 2005
 
Z. W. Hall "Introduction to Molecular Neurobiology", Sinauer Associates Incorporated, 1992
 
J. Malmivuo und R. Plonsey "Bioelectromagnetism", Oxford University Press, 1999
 
M. Abeles "Corticonics: Neural Circuits of the Cerebral Cortex", Cambridge University Press, 1991
 
M. F. Bear, B. W. Connors und M. A. Paradiso "Neuroscience", Lippincott Williams and Wilkins, 2001
 
P. S. Churchland and T. J. Sejnowski "The Computational Brain", MIT Press, 1992
 
P. Dayan and L.F. Abbott "Theoretical Neuroscience", MIT Press, 2001
 
C. Eliasmith and Ch. H. Anderson "Neural Engineering", MIT Press, 2003
 
Ch. Koch "Biophysics of Computation", Oxford University Press, 1999
 
Akay, M. (Ed.): Time Frequency and Wavelets in Biomedical Signal Processing, IEEE Computer Society Press, 1997
 
Azizi, S.A.: Entwurf und Realisierung digitaler Filter, Oldenbourg, 1990
 
Bruce, Eugene N.: Biomedical Signal Processing and Signal Modeling, John Wiley & Sons, 2001
 
Mertins, A.: Signaltheorie, Springer Vieweg Wiesbaden, 1996
 
Oppenheim, Alan V.; Schafer, Ronald W.; Buck, John R.: Zeitdiskrete Signalverarbeitung, Oldenbourg, (akt.  Aufl.)
 
Semmlow, John L.: Biosignal and Biomedical Image Processing, Marcel Dekker, 2004
 
Vetterli, Martin; Kovacevic, Jelena: Wavelets and Subband Coding, Prentice Hall, 1995

[updated 21.04.2026]
 
[Wed Apr 29 10:05:27 CEST 2026, CKEY=bngdn, BKEY=bmt4, CID=BMT3502.NIS, LANGUAGE=en, DATE=29.04.2026]