13335 - Brain-Computer Interfaces (BCIs) for Neuroadaptive Technology Modulübersicht

Module Number: 13335
Module Title:Brain-Computer Interfaces (BCIs) for Neuroadaptive Technology
  Brain-Computer-Interfaces für Neuroadaptive Technologien
Department: Faculty 1 - Mathematics, Computer Science, Physics, Electrical Engineering and Information Technology
Responsible Staff Member:
  • Prof. Dr. rer. nat. Zander, Thorsten O.
Language of Teaching / Examination:English
Duration:1 semester
Frequency of Offer: Every semester
Credits: 6
Learning Outcome:After successfully completing the module, students possess a basic understanding of the methodology of Brain-Computer Interfaces (BCIs), including measurement of brain activity, signal processing, machine learning and the principle of automated interpretation of brain activity to assess information of changes in cognitive states. Furthermore, they are familiar with the use of BCIs in current and to-be-created human-computer interactions which includes the current development of beneficial Artificial Intelligence.
Contents:The module will consist of lectures describing the methodology and use of Brain-Computer Interfaces from the scratch. This includes knowledge from machine learning and signal processing, as well as psychophysiology and psychology, and human-computer interaction.
In the seminar, students will be introduced to AI safety and the ethics of neurotechnology, and will prepare group presentations on various related topics and issues. Ethical issues and social consequences are discussed and guidelines for research and development are derived.
Recommended Prerequisites:None
Mandatory Prerequisites:Passing the exam of module 
  • 13942: Foundations of Psychophysiology
Forms of Teaching and Proportion:
  • Lecture / 2 Hours per Week per Semester
  • Seminar / 2 Hours per Week per Semester
  • Self organised studies / 120 Hours
Teaching Materials and Literature:
  • Zander, T. O. (2011). Utilizing Brain-Computer Interfaces for Human-Machine Systems (Doctoral dissertation, Universitätsbibliothek der Technischen Universität Berlin).
Module Examination:Continuous Assessment (MCA)
Assessment Mode for Module Examination:
  • active participation by asking questions during or after the classes (20%)
  • moderated discussion of selected topics related to the lecture, 45 minutes (30%)
  • written exam, 60 minutes (50%)
Evaluation of Module Examination:Performance Verification – graded
Limited Number of Participants:72
Part of the Study Programme:
  • Abschluss im Ausland / Artificial Intelligence / keine PO
  • Master (research-oriented) / Artificial Intelligence / PO 2022
  • Master (research-oriented) / Informatik / PO 2008
  • Master (research-oriented) / Informations- und Medientechnik / PO 2017
  • Master (research-oriented) / Künstliche Intelligenz Technologie / PO 2022
  • Bachelor (research-oriented) / Medizininformatik / PO 2016
Remarks:
  • Study programme Medizininformatik: B.Sc.: Compulsory elective module in complex „Medizininformatik"
  • Study programme Informatik M.Sc.: Compulsory elective module in complex „Praktischer Informatik" (level 400)
  • Study programme Artificial Intelligence M.Sc.: Compulsory elective module in complex „Learning and Reasoning“
  • Study programme  Künstliche Intelligenz Technologie B.Sc.: Compulsory elective module in complex „Kognitions- und Neurowissenschaft“
Module Components:
  • Lecture: Brain-Computer Interfaces (BCIs) for Neuroadaptive Technology
  • Accompanying seminar
Components to be offered in the Current Semester: