052320 VU Advanced Topics in Data Analysis (2019S)

6.00 ECTS (4.00 SWS), SPL 5 - Informatik und Wirtschaftsinformatik

Prüfungsimmanente Lehrveranstaltung

Moodle

An/Abmeldung

Hinweis: Ihr Anmeldezeitpunkt innerhalb der Frist hat keine Auswirkungen auf die Platzvergabe (kein "first come, first served").

Anmeldung von Mo 11.02.2019 09:00 bis Mi 20.02.2019 23:59
Abmeldung bis So 17.03.2019 23:59

Details

max. 25 Teilnehmer*innen

Sprache: Englisch

Lehrende

Moritz Grosse-Wentrup

Termine (iCal) - nächster Termin ist mit N markiert

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.

    
    Montag
    20.05.
    08:30 - 17:30
    PC-Unterrichtsraum 1, Währinger Straße 29 1.UG
    
    Dienstag
    21.05.
    08:30 - 17:30
    PC-Unterrichtsraum 1, Währinger Straße 29 1.UG
    
    Mittwoch
    22.05.
    08:30 - 17:30
    Seminarraum 2, Währinger Straße 29 1.UG
    
    Montag
    27.05.
    08:30 - 17:30
    PC-Unterrichtsraum 1, Währinger Straße 29 1.UG
    
    Dienstag
    28.05.
    08:30 - 17:30
    PC-Unterrichtsraum 1, Währinger Straße 29 1.UG
    
    Mittwoch
    29.05.
    08:00 - 12:30
    Seminarraum 10, Währinger Straße 29 2.OG
    
    Donnerstag
    06.06.
    14:00 - 17:30
    Seminarraum 10, Währinger Straße 29 2.OG
    
    Donnerstag
    13.06.
    13:15 - 14:45
    Seminarraum 5, Währinger Straße 29 1.UG

Information

Ziele, Inhalte und Methode der Lehrveranstaltung

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.

Art der Leistungskontrolle und erlaubte Hilfsmittel

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.

Mindestanforderungen und Beurteilungsmaßstab

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).

Prüfungsstoff

Literatur

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

Zuordnung im Vorlesungsverzeichnis

Module: AT-DA

Letzte Änderung: Mo 07.09.2020 15:30