Modulbezeichnung |
Human Computer Interaction |
Modulkürzel |
psy220 |
Kreditpunkte |
6.0 KP |
Workload |
180 h
|
Einrichtungsverzeichnis |
Department für Psychologie |
Verwendbarkeit des Moduls |
-
Master Neurocognitive Psychology (Master) > Mastermodule
|
Zuständige Personen |
-
Rieger, Jochem (Modulverantwortung)
-
Rieger, Jochem (Prüfungsberechtigt)
|
Teilnahmevoraussetzungen |
Enrolment in Master's programme Neurocognitive Psychology or other programs related to the field (e.g. neuroscience, computer science, physics etc.). |
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 |
Modulinhalte |
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.
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. |
Literaturempfehlungen |
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. |
Links |
|
Unterrichtssprache |
Englisch |
Dauer in Semestern |
1 Semester |
Angebotsrhythmus Modul |
The module will be offered every summer term. |
Aufnahmekapazität Modul |
15 |
Hinweise |
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! |
Modulart |
Wahlpflicht / Elective |
Modullevel |
MM (Mastermodul / Master module) |
Lehr-/Lernform |
Part 1: lecture; Part 2: practical seminar |
Vorkenntnisse |
Basic programming skills, some high-school level maths |