psy220 - Human Computer Interaction (Complete module description)
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 |
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Responsible persons |
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Prerequisites | 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. 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 |
Module contents | The module will introduce classic BCI paradigms and brain recoding techniques. However the main focus will be on a deeper understanding of the most important signal processing, machine learning, and performance evaluation techniques. The module combines a lecture on the theoretical foundations a seminar/hands on course in which students learn to implement the BCI-processing steps on real neurophysiological data and further elaborate specific subtopics.
Where possible the lecture provides mathematical backgrounds of the data analysis techniques. |
Recommended reading | 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) 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. 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. 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 BCI 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. |
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Language of instruction | English |
Duration (semesters) | 1 Semester |
Module frequency | The module will be offered every summer term. |
Module capacity | 15 |
Reference text | PLEASE NOTE: We strongly recommend to take either psy170, psy270, psy280, psy220 or psy290 to gain methodological competencies (EEG, fMRI, TBS, HCI, ambulatory assessment techniques) that are needed for most practical projects and Master's theses! |
Type of module | Wahlpflicht / Elective |
Module level | MM (Mastermodul / Master module) |
Teaching/Learning method | Part 1: lecture; Part 2: practical seminar |
Previous knowledge | Basic programming skills, some high-school level maths |
Type of course | Comment | SWS | Frequency | Workload of compulsory attendance |
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Lecture | 2 | SuSe | 28 | |
Seminar | 2 | SuSe | 28 | |
Total module attendance time | 56 h |
Examination | Prüfungszeiten | Type of examination |
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Final exam of module | last lecture in summer term |
The module will be evaluated with an oral exam (max. 20 min). |