Universität Wien

052320 VU Advanced Topics in Data Analysis (2019S)

6.00 ECTS (4.00 SWS), SPL 5 - Informatik und Wirtschaftsinformatik
Continuous assessment of course work
Moodle

Registration/Deregistration

Note: The time of your registration within the registration period has no effect on the allocation of places (no first come, first served).

Details

max. 25 participants
Language: English

Lecturers

Classes (iCal) - next class is marked with N

Dates and topics will be announced in the beginning of March. Please sign up in advance. After the dates and topics have been announced, there will be an opportunity to sign off again.

  • Monday 20.05. 08:30 - 17:30 PC-Unterrichtsraum 1, Währinger Straße 29 1.UG
  • Tuesday 21.05. 08:30 - 17:30 PC-Unterrichtsraum 1, Währinger Straße 29 1.UG
  • Wednesday 22.05. 08:30 - 17:30 Seminarraum 2, Währinger Straße 29 1.UG
  • Monday 27.05. 08:30 - 17:30 PC-Unterrichtsraum 1, Währinger Straße 29 1.UG
  • Tuesday 28.05. 08:30 - 17:30 PC-Unterrichtsraum 1, Währinger Straße 29 1.UG
  • Wednesday 29.05. 08:00 - 12:30 Seminarraum 10, Währinger Straße 29 2.OG
  • Thursday 06.06. 14:00 - 17:30 Seminarraum 10, Währinger Straße 29 2.OG
  • Thursday 13.06. 13:15 - 14:45 Seminarraum 5, Währinger Straße 29 1.UG

Information

Aims, contents and method of the course

Neuroinformatics develops and uses machine learning methods to study the brain. One important application of these methods is in the field of brain-computer interfacing (BCI). By decoding intentions from brain-imaging data, BCI can assist severely paralyzed patients in communication, support rehabilitation after stroke, and enhance human-computer interaction. In this course, we will study all aspect of BCI, including but not limited to:

1. Basic neurophysiology
2. Techniques for recording brain activity
3. Experimental paradigms in BCI
4. Biomedical signal processing and feature generation
5. Spatial filtering methods
6. Brain decoding methods
7. (Neuro-)feedback design
8. Software toolboxes for BCI design
9. Applications of BCI in communication, rehabilitation and human-computer interaction

The course will consist of lectures on these topics, in-class pen & paper exercises to deepen the theoretical understanding of the mathematical methods, and Python-based programming exercises on real brain-imaging data.

The ultimate goal of this course is to form a student team for the upcoming Cybathlon 2020 (http://www.cybathlon.ethz.ch/).

The entire course will be taught in English.

Assessment and permitted materials

Following the six blocks, there will be a final written 90-minutes exam. The exam will focus on questions that test the depth of understanding rather than the extent of memorization of the studied topics. Accordingly, all important formula will be provided in the exam and no additional resources beyond a pen are allowed in the exam. The exam will be held in English.

Minimum requirements and assessment criteria

Grading will be done according to the following scheme:

1 – at least 87.5%
2 – at least 75.0%
3 - at least 60.0%
4 – at least 40.0%

In order to successfully pass the course, regular attendance is strongly recommended (but not mandatory).

Examination topics

1. Basic neurophysiology
2. Techniques for recording brain activity
3. Experimental paradigms in BCI
4. Biomedical signal processing and feature generation
5. Spatial filtering methods
6. Brain decoding methods
7. (Neuro-)feedback design
8. Software toolboxes for BCI design
9. Applications of BCI in communication, rehabilitation and human-computer interaction

Reading list

Rajesh P. N. Rao., Brain-Computer Interfacing: An Introduction (1st edition). Cambridge University Press, 2013.

Association in the course directory

Module: AT-DA

Last modified: Mo 07.09.2020 15:30