Stud.IP Uni Oldenburg
University of Oldenburg
07.05.2021 02:48:34
psy220 - Human Computer Interaction (Course overview)
Department of Psychology 6 KP
Module components Semester courses Sommersemester 2018 Examination
Lecture
Seminar
  • No access 6.02.220_2 - HCI and BCI in practice Show lecturers
    • Alexander Dreyer, M. Sc.
    • Prof. Dr. Jochem Rieger

    Wednesday: 12:00 - 14:00, weekly (from 04/04/18)

    Participation is possible after registration in VA 6.02.220_1 (only 15 places)

Notes for the module
Prerequisites
Enrolment in Master's programme Neurocognitive Psychology or other programs related to the field (e.g. neuroscience, computer science, physics etc.).
Reference text
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!
Capacity / number of participants 15
Time of examination
last lecture in summer term
Module examination
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).
Skills to be acquired in this module
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. Moreover, BCI-techniques can be used for modern data-driven basic neuroscience.
The module combines a lecture on the theoretical foundations of the most important techniques
with a seminar/hands on course in which students learn to implement the BCI-processing steps
on real neurophysiological data and further elaborate specific subtopics.


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