Stud.IP Uni Oldenburg
Universität Oldenburg
29.06.2022 18:38:52
psy220 - Human Computer Interaction (Veranstaltungsübersicht)
Department für Psychologie 6 KP
Modulteile Semesterveranstaltungen Sommersemester 2022 Prüfungsleistung
Vorlesung
Seminar
  • Eingeschränkter Zugang 6.02.220_2 - Hands- on BCI implementation Lehrende anzeigen
    • Prof. Dr. Jochem Rieger

    Mittwoch: 12:15 - 13:45, wöchentlich (ab 20.04.2022)

    Participation is possible after registration in VA 6.02.220_1 (only 15 places) Please do not send any queries on waiting list. Many Thanks!

Hinweise zum Modul
Teilnahmevoraussetzungen
Enrolment in Master's programme Neurocognitive Psychology or other programs related to the field (e.g. neuroscience, computer science, physics etc.).
Hinweise
We strongly recommend to take either psy170, psy270, psy280, or psy220 to gain methodological competencies (EEG, fMRI, TBS, HCI) that are needed for most practical projects and Master's theses!
Kapazität/Teilnehmerzahl 15
Prüfungszeiten
last lecture in summer term
Prüfungsleistung Modul
The module will be evaluated with an oral exam (max. 20 min). 

Required active participation for gaining credits:
1-2 presentations
max. 24 programming exercises in the seminar
participation in discussions on other presentations
attendance of at least 70% in the seminar (use attendance sheet that will be handed out in the beginning of the term).
Kompetenzziele
Goals of module:
The goal of the module is to provide students with basic skills required to plan, implement and
evaluate brain computer interfaces as devices for human computer interaction. BCIs are an
ideal showcase as they fully span the interdisciplinary field of HCI design, implementation and
evaluation. Importantly, classical BCI-methods can be used for modern data-driven basic neuroscience. 
The module is designed as an "enabler course", meaning that ideally students should be able to understand and start independent studies into the BCI-methods. Therefore, it goes into depth instead of breadth. Good programming skills and some active knowledge of high school maths is strongly advised to maximize the learning outcome.

Competencies:
++ understanding of the foundations of statistical learning techniques
+ basics to understand technical time series processing and machine learning papers
++ interdisciplinary knowledge & thinking
+ experimental methods
++ statistics & scientific programming
+ critical & analytical thinking
+ scientific communication skills
+ knowledge transfer
+ group work
+ project & time management