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University of Oldenburg
01.12.2021 17:38:25
psy220 - Human Computer Interaction
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Module label Human Computer Interaction
Module code psy220
Credit points 6.0 KP
Workload 180 h
Institute directory Department of Psychology
Applicability of the module
  • Master's Programme Neurocognitive Psychology (Master) > Mastermodule
Responsible persons
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
Module contents
Part 1: HCI and BCI Lecture: (Lecture on methodological foundations of BCI): summer

Part 2: Hands on BCI implementation (practical seminar): summer

Topics covered:
  • A brief history of BCIs and examples of HCI control and basic neuroscience using BCI
  • techniques.
  • Data preprocessing (e.g. filtering, projection techniques) and common artifacts and
  • artifact treatment)
  • Feature generation (e.g. fourier transform, spectral estimation techniques, principle
  • components)
  • Machine learning for classification and regression (e.g. model parameter optimization in
  • multivariate regression)
  • Evaluation (e.g. measures of model quality, cross validation to test model generalization,
  • permutation tests)
Where possible the lecture provides mathematical backgrounds of the data analysis techniques.
The practical seminar implements BCI techniques on a real data set and further elaborates
specific topics in seminar form.
Reader's advisory
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. (open access)

Signal processing:

Semmlow, J. L. (2008). Biosignal and medical image processing. CRC press. Basis of most of
the signal processing section. Has some matlab code.


Shlens, Jonathon. 2014. “A Tutorial on Principal Component Analysis.” ArXiv:1404.1100 [Cs,
Stat], April. Great accessible tutorial on PCA

Unsupervised feature Learning and deep learning tutorial: Basis of the multivariate machine learning techniques.
Has some matlab code.

General texts:

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.
Module capacity 15
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!
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
2 SuSe 28
2 SuSe 28
Total time of attendance for the module 56 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:
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).