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Advances in Brain-Computer-Interfaces


learning results of the module

ID study objective
L1 The ability to describe and model biological and technical systems mathematically
L2 Methods of signal acquisition and processing in the fields of psychophysiology, clinical neurophysiology and audiology.
L3 Understanding of how technical components and systems operate at the bio-technical interface and in human-machine interfaces. Knowledge of neural and cognitive-affective parameters in brain-computer interfaces, neuroprosthetics and neurostimulation
L4 Ability to design appropriate technical solutions, taking into account the specific aspects of the interaction between technical systems and the human body
L6 Fundamental principles and mechanisms of machine learning and neuro-inspired artificial intelligence (e.g. neural networks and common machine learning methods) and their applicability to practical problems. Understanding of the similarities and differences between mathematical neuron models, modern AI techniques and machine learning
L12 The ability to work in a team, key social and intercultural skills
L13 Project and planning skills, the ability to make independent decisions whilst taking into account the technical, medical, economic and social context and possibilities.

Module name (EN):
Name of module in study programme. It should be precise and clear.
Advances in Brain-Computer-Interfaces
Degree programme:
Study Programme with validity of corresponding study regulations containing this module.
Neural Engineering, Master, regulation 01.10.2025
Module code: NE2213.BCI
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.
P213-0204
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.
1V+5P (6 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.
6
Semester: 2
Mandatory course: no
Language of instruction:
English
Assessment:
Presentation

[updated 15.06.2026]
Applicability / Curricular relevance:
All study programs (with year of the version of study regulations) containing the course.

NE2213.BCI (P213-0204) Neural Engineering, Master, regulation 01.04.2020 , optional course
NE2213.BCI (P213-0204) Neural Engineering, Master, regulation 01.10.2025 , semester 2, optional 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).
90 class hours (= 67.5 clock hours) over a 15-week period.
The total student study time is 180 hours (equivalent to 6 ECTS credits).
There are therefore 112.5 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:
Dozierende der Vertiefungsrichtung


[updated 18.06.2026]
Learning outcomes:
The module provides students with the opportunity to independently (yet supervised) develop and implement a project in the field of modern Brain-Computer Interface (BCI) approaches based on current research findings and scientific publications. The module covers the complete workflow, from initial idea generation to the final presentation of results.
The students documented their progress of the work continuously. Special emphasis is placed on a structured approach, the sound justification of methodological decisions, and the self-organized collaboration within the group.

[updated 15.06.2026]
Module content:
Students independently select a suitable, application-oriented topic with cross-domain aspects from a set of predefined topic areas. Based on a targeted literature review, they develop a well-founded project proposal that goes beyond basic approaches and is aligned with current methods, applications, or research challenges. Subsequently, the project is conceptually developed and practically implemented.
 
• Targeted literature research and review in relevant databases to utilize scientific publications for the development of a project.
• Development of a well-founded project proposal that is aligned with current methods and research questions and goes beyond fundamental approaches.
• Independently identify and implement suitable methodologies for addressing a project topic in the field of Brain-Computer Interfaces.
• Conceptualization of a project plan and subsequent implementation. Justification of methodological decisions in a transparent and comprehensible manner.
• Structured documentation of the entire work process.
• Presentation of project results and discussion of technical issues.

[updated 15.06.2026]
Teaching methods/Media:
Lecture and student-centered project phase

[updated 15.06.2026]
Recommended or required reading:
• Guger, C.; Allison, B.; Lotte, F. Korostenskaja, M. Eds., "Brain-Computer Interface Research - A State-of-the-Art Summary 13 - Jul 2026", Springer Briefs in Electrical and Computer Engineering, 2026
• Cohen M.X., "Analyzing Neural Time Series Data: Theory and Practice", Issues in Clinical and Cognitive Neuropsychology, MIT Press, 2014
• Afifi, A.K., "Functional Neuroanatomy", McGraw-Hill Education, 2005
 
Project specific literature recommendations

[updated 18.06.2026]
 
[Sat Oct 31 19:12:01 CET 2026, CKEY=naib, BKEY=nem2, CID=NE2213.BCI, LANGUAGE=en, DATE=31.10.2026]