|Module label||Human Computer Interaction|
|Credit points||6.0 KP|
|Institute directory||Department of Psychology|
|Applicability of the module||
Rieger, Jochem (Module responsibility)
Rieger, Jochem (Authorized examiners)
Enrolment in Master's programme Neurocognitive Psychology or other programs related to the field (e.g. neuroscience, computer science, physics etc.).
|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.
++ 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
Part 1: HCI and BCI Lecture: (Lecture on methodological foundations of BCI): summer
Part 2: Hands on BCI implementation (practical seminar): summer
The practical seminar implements BCI techniques on a real data set and further elaborates
specific topics in seminar form.
There is no required textbook. The lecture slides and notes should be sufficient. However some
resources from which they were developed on are given below:
General tutorial text providing and overview and accompanying python code on github:
Holdgraf, Christopher R., Jochem W. Rieger, Cristiano Micheli, Stephanie Martin, Robert T.
Knight, and Frederic E. Theunissen. 2017. “Encoding and Decoding Models in Cognitive
Electrophysiology.” Frontiers in Systems Neuroscience 11.
https://doi.org/10.3389/fnsys.2017.00061. (open access)
Semmlow, J. L. (2008). Biosignal and medical image processing. CRC press. Basis of most of
the signal processing section. Has some matlab code.
PCA & SVD
Shlens, Jonathon. 2014. “A Tutorial on Principal Component Analysis.” ArXiv:1404.1100 [Cs,
Stat], April. http://arxiv.org/abs/1404.1100. Great accessible tutorial on PCA
Unsupervised feature Learning and deep learning tutorial:
http://deeplearning.stanford.edu/tutorial/ Basis of the multivariate machine learning techniques.
Has some matlab code.
Machine learning and AI:
Hastie, Tibshirani, and Friedman. The elements of statistical learning. Covers a wide range of
machine learning topics. Free online.
Russell and Norvig. Artificial Intelligence: A Modern Approach. A comprehensive reference
Dornhege et al. (2007) Toward Brain Machine Interfacing, The MIT-Press. A collection of essays
on BCI related topics.
Additional literature and material will be provided on the course website.
|Language of instruction||English|
|Duration (semesters)||1 Semester|
|Module frequency||The module will be offered every summer term.|
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!
|Modullevel / module level||MM (Mastermodul / Master module)|
|Modulart / typ of module||Wahlpflicht / Elective|
|Lehr-/Lernform / Teaching/Learning method||Part 1: lecture; Part 2: practical seminar|
|Vorkenntnisse / Previous knowledge||Basic programming skills, some high-school level maths|
|Course type||Comment||SWS||Frequency||Workload of compulsory attendance|
|Total time of attendance for the module||0 h|
|Examination||Time of examination||Type of examination|
|Final exam of module||
last lecture in summer term
The module will be evaluated with an oral exam (max. 20 min).
Required active participation for gaining credits:
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).