Author: Philipp Wicke

Lecturer: Klaus Gramann
Fields: Cognitive Neuroscience, Mobile Brain Imaging

Content

The human brain has evolved to optimize the outcome of our behavior. Yet, established human brain imaging approaches restrict any active movement of participants to avoid artifacts from distorting the signal of interest. Recent developments in brain imaging technologies allow for conducting experiments beyond established laboratory-based experimental protocols. Light-weight mobile EEG and fNIRS amplifiers can be combined with additional modalities like motion capture, eye tracking and virtual reality providing unprecedented insights into behavioural and brain dynamic states during embodied interactions with our surroundings.
The course will introduce Mobile Brain/Body Imaging (MoBI). The core knowledge and skills taught by the course are:
• the fundamental concepts behind EEG and problems related to movement
• the basic concepts of MoBI (embodiment, technology, applications)
• core ideas and findings in MoBI research
• application of MoBI to the field of spatial cognition

In more detail, the course will have four sessions with the following topics:
1. Fundamental EEG Concepts: physiological origins of the EEG signal, generators, volume and capacitive conduction, oscillations and origins, extraction of time domain and frequency domain parameters
2. Fundamental EEG Concepts: EEG technology, traditional amplifiers, newer developments
3. Basic concepts of MoBI: Embodiment, EEG and movement, multimodal Data acquisition, multimodal data analyses
4. MoBI application: Embodied spatial navigation, MoBI and traditional desktop comparison, newer developments

Literature

• Literature is optional and more regarded as ‘further/complementary reading’:
• 1) Wilson, M. (2002). Six views of embodied cognition. Psychonomic bulletin & review, 9(4), 625-636.
• 2) Niso, G., Romero, E., Moreau, J. T., Araujo, A., & Krol, L. R. (2023). Wireless EEG: A survey of systems and studies. NeuroImage, 269, 119774.
• 3) Makeig, S., Gramann, K., Jung, T.-P., Sejnowski, T.J., & Poizner, H. (2009). Linking Brain, Mind and Behavior. International Journal of Psychophysiology, 73(2), 95-100.
• 4) Gramann, K. (2024). Mobile EEG for Neurourbanism Research-What Could Possibly Go Wrong? A Critical Review with Guidelines. Journal of Environmental Psychology, 102308.

Lecturer

Klaus Gramann

seit 07/2012 Professor of Biological Psychology and Neuroergonomics, Technical University Berlin, Germany 10/2011 – 04/2012 Acting Professor of Cognitive Psychology, University Osnabrück, Germany 05/2011 – 10/2011 Visiting Professor, National Chiao Tung University, Hsinchu, Taiwan 07/2011 – 12/2011 Associate Research Scientist, University of California, San Diego, USA 05/2007 – 07/2011 Assistant Research Scientist, University of California, San Diego, USA 03/2004 – 05/2007 Assistant Professor (C1), University Munich, Germany 06/2002 – 03/2004 Post Doctoral Scholar, University Munich, Germany 2002 – 2007 Habilitation Biological and General Psychology, University Munich, Germany 1998 – 2002 Ph.D. Psychology, Technical University Aachen, Germany 1998 – 1998 Diploma Psychology, Justus Liebig University Gießen, Germany 1994 – 1996 Pre-Diploma Psychology, Justus Liebig University Gießen, Germany 1991 – 1993 Certified Communication Manager, Academy for Communication, Kassel, Germany

Affiliation: TU Berlin
Homepage: https://www.tu.berlin/en/bpn/about/management-and-administration/klaus-gramann

Lecturer: Sven Mayer, Matthias Schmidmaier
Fields: Artificial Intelligence, Human-Computer Interaction, Human-AI Interaction

Content

The course Introduction to Intelligent User Interfaces (IUI) introduces participants to key concepts at the intersection of Human-Computer Interaction (HCI) and Artificial Intelligence (AI). It explores how methods from Machine Learning and AI can be transferred to the design of interactive systems that act intelligently, adapt to users, and support human goals. Emphasis is placed on a human-centered perspective that prioritizes usability, transparency, and user trust. Across four sessions, participants will gain a conceptual understanding of the foundations, design principles, and open challenges of intelligent user interfaces, preparing them to critically assess and discuss current and future developments in this field.
* 1. Session: Motivation and Introduction
* 2. Session: Machine Learning and Human-Computer Interaction basics
* 3. Session: Designing, Building, and Evaluating Human-AI Systems
* 4. Session: Human-Centered Challenges and Future Directions

Literature

• Andy Field and Graham Hole (2002). How to Design and Report Experiments
• Kasper Hornbæk, Per-Ola Kristensson, and Antti Oulasvirta (2025). Introduction to Human-Computer Interaction. Oxford University Press.
• Course: Intelligent User Interfaces, https://iui-lecture.org/
• Course: Practical Machine Learning, https://sven-mayer.com/pml/
• Course: Human-Computer Interaction, https://hci-lecture.org/

Lecturer

Sven Mayer is a full professor of computer science at the TU Dortmund University (Germany) and the Research Center Trustworthy Data Science and Security, where he is the head of the chair for Human-AI Interaction. His research focuses on Human-AI Interaction at the intersection between Human-Computer Interaction and Artificial Intelligence, where he focuses on the next generation of computing systems. He uses artificial intelligence to design, build, and evaluate future human-centered interfaces. In particular, he envisions enabling humans to outperform their performance in collaboration with the machine. He focuses on areas such as augmented and virtual reality, mobile scenarios, and robotics.

Affiliation: TU Dortmund University
Homepage: https://sven-mayer.com/

Matthias Schmidmaier is a research engineer in the Human-AI Interaction group of Prof. Sven Mayer at the Research Center Trustworthy Data Science and Security, TU Dortmund University, with a research affiliation at LMU Munich. His current work explores empathic interaction with intelligent systems, such as mental health chatbots or social robots, investigating the need, impact, and sources of perceived empathy and related constructs. Alongside his academic research, he brings over a decade of industry experience developing human-centered technologies in affective computing, vision-based behavior analytics, and XR, across both dynamic startup environments and collaborations with major international companies.

Affiliation: TU Dortmund University
Homepage: https://schmidmaier.org/

Lecturer: Katharina Krämer, Annekatrin Vetter, Sophia Reul
Fields: Psychology, Psychotherapy

Content

Where do apparently opposite qualities of experience show up in our inner lives? How do thinking and feeling fit together — and where don’t they? What shapes the connection between body and mind? How do we experience the outer world, and what do we experience within — and how can these be linked and integrated? What stays unconscious, and what becomes conscious?
These and similar questions are at the heart of our self-experience workshop. Based on experiential exercises drawing from psychoanalysis, humanistic psychology, and body-oriented approaches, participants are invited into a reflective space to explore self-awareness, perception, and communication. No prior knowledge is required — all you need is a little curiosity and a willingness to gently step beyond the edge of your comfort zone. Then self-experience can become a bridge to new realities of being and relating.

Lecturer

Katharina Krämer is a psychologist and analytic psychotherapist. She works as a professor for psychology at the Rheinische Hochschule Köln, University of Applied Sciences, Cologne, Germany, and as a lecturer and supervisor for psychotherapists in training. Additionally, she works as a psychotherapist in private practice. In 2014, Katharina Krämer received her doctoral degree from the University of Cologne, Germany, on a thesis investigating the perception of dynamic nonverbal cues in cross-cultural psychology and high-functioning autism. Her research interests include the application of Mentalization-Based Group-Therapy with patients with autism and the vocational integration of patients with autism.

Affiliation: Rheinische Hochschule Köln, University of Applied Sciences
Homepage: https://rh-koeln.de/dozierende/katharina-krmer

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Annekatrin Vetter is a clinical psychologist and analytic psychotherapist. As a psychotherapist, she treats patients with different mental disorders in private practice. Additionally, she works as a lecturer and supervisor for psychotherapists in training and as a trainer for Coaches at Inscape – Coaching & Counselling, Cologne, Germany.

Affiliation: Praxis für Psychotherapie und Psychoanalyse, Supervision und Coaching Annekatrin Vetter, Cologne

Sophia Reul is a clinical psychologist and analytic psychotherapist. She works as a psychotherapist in private practice. In 2021, she received her doctoral degree from the Westfälische Wilhelms-University Münster, Germany, on a thesis investigating the impact of neuropsychological methods in diagnoses of early dementia. Today, her research interests include the application of Mentalization-Based Group-Therapy (MBT-G) with patients with autism.

Affiliation: Praxis für Psychotherapie und Psychoanalyse Sophia Reul, Kirchweidach (Bay.)

Lecturer: Terrence Stewart
Fields: Computational Neuroscience, Neuroscience, AI

Content

This course provides an overview of computational neuroscience, the science of creating computer simulations of neurons, groups of neurons, and different brain systems, and then comparing the results of these simulations to the behaviour of real brains. This lets us better understand how brains work, and it also has the potential of inspiring new types of Artificial Intelligence systems.

We start by looking at individual neurons and their details, then move to the three major approaches to making large-scale models capable of producing detailed behaviour: Parallel Distributed Processing (PDP++/Emergent), Dynamic Neural Fields (DNF/Cedar), and the Neural Engineering Framework (NEF/Nengo). Python notebooks will be provided for hands-on examples.

Session 1: Individual neurons
Session 2: Many neurons in parallel (PDP++)
Session 3: Dynamic Neural Fields (DNF)
Session 4: The Neural Engineering Framework and Nengo

Literature

• Kriegeskorte, N., & Douglas, P. K. (2018). Cognitive computational neuroscience. Nature neuroscience, 21(9), 1148–1160. https://doi.org/10.1038/s41593-018-0210-5
• Rumelhart, D., & McClelland, J., (1986). Parallel distributed processing: Explorations in the microstructure of cognition. MIT Press, Cambridge, MA, USA
• Schöner, G. (2023). Dynamical Systems Approaches to Cognition. In Sun, Ron (Ed.), The Cambridge Handbook of Computational Cognitive Sciences (2nd ed.). Cambridge University Press.
• Stewart, T.C., & Eliasmith, C. (2014). Large-scale synthesis of functional spiking neural circuits. Proceedings of the IEEE, 102(5):881–898.

Lecturer

Terry Stewart is a Senior Research Officer at the National Research Council Canada, and Site Lead of the NRC-University Waterloo Collaboration Centre. His research includes large-scale brain simulation, cognitive modelling, energy-efficient neuromorphic computing, and AI safety.

Affiliation: National Research Council Canada

Lecturer: Mike Ambinder
Fields: BCI, Video Games, Neuroscience, AI, ML, Engineering

Content

This course will cover one possible future for video game play. At the moment, video games provide a dynamic experience on several axes, but they remain relatively static with respect to the individual player experience. They are designed for the collective – they do not adapt. With the advent of improved hardware, statistical techniques, and advances in game design, the potential exists to design a new generation of gameplay where the experience is tailored to the individual as a consequence of physiological measurement of internal state. Under this framework; games may become capable of a whole lot more than entertainment.

Literature

• Ambinder, Mike (2019) Brain-Computer Interfaces: One Possible Future For How We Might Play https://www.youtube.com/watch?v=Qhj3C1H5JWo

Lecturer

Dr. Mike Ambinder received a BA in Computer Science and Psychology from Yale University and a PhD in Psychology (Visual Cognition) from The University of Illinois. He spent 15 years at Valve leading research efforts in applied psychology and game design, statistics, machine learning, and AI, economic systems design, and Brain-Computer Interfaces. He is currently the Chief Research Officer of Cognitive Explorations, a design consultancy in the games space, and the Chief Research Officer of August Interactive, a gaming startup with a focus on prosocial behavior change.

Affiliation: Cognitive Explorations, LLC; University of Washington; August Interactive

Lecturer: Timothy Drysdale
Fields: Artificial intelligence, education, practical work

Content

This evening talk will reflect on the challenge facing educators, particularly younger educators with many years of teaching ahead of them. The joint pressure of readily-available artificial intelligence affecting the validity of traditional processes, and massification of education reducing the resources available per student, pose a difficult pinch point that is generating demand for authentic, interactive activities but placing a lot of pressure on the available time and space for students to experiment with real equipment in a traditional manner. I\’ll introduce a solution in the form of laboratories in a box, which we have been doing doing at the University of Edinburgh for a number of years, and describe the elements that make these successful for us, how you can adopt a similar approach, the pitfalls to avoid and some fruitful future directions for our communities of educators to explore, in particular in expanding what we do with the data streams to support better learning and in taking our concept of experiments beyond what we are used to doing in traditional laboratories.

Literature

• Reid, D., & Drysdale, T. (2024). Student-facing learning analytics dashboard for remote lab practical work. IEEE Transactions on Learning Technologies, 17, 1037-1050. https://doi.org/10.1109/TLT.2024.3354128
• D.Reid, J. Burridge, D. Lowe, T. Drysdale “Open-source remote laboratory experiments for controls engineering education,” International Journal of Mechanical Engineering Education. February 2022. doi:10.1177/03064190221081451
• T. D. Drysdale, S. Kelley, A.-M. Scott, V. Dishon, A. Weightman, R. J. Lewis & S. Watts “Opinion piece: non-traditional practical work for traditional campuses,” Higher Education Pedagogies, 5:1, 210-222, 2020, DOI: 10.1080/23752696.2020.1816845
• G. L. Knight & T. D. Drysdale The future of higher education (HE) hangs on innovating our assessment – but are we ready, willing and able?, Higher Education Pedagogies, 5:1, 57-60, 2020, DOI: 10.1080/23752696.2020.1771610

Lecturer

Prof Timothy Drysdale is the Chair of Technology Enhanced Science Education and Director of Strategic Digital Education in the School of Engineering. His main research activity is in Engineering Education, where he leads the Remote Laboratories group. He and his team have developed an entirely new infrastructure and approach for operating online remote laboratories on traditional campuses (practable.io), winning international awards from the Global Online Laboratories Consortium (Remote Experiment Award 2024) and the Association for Learning Technology / Jisc Award for Digital Transformation in 2023. His prior research activities were in the area of terahertz component design and testing, microwave antennas, and optical plasmonics. He has a long-standing involvement with public outreach in science and engineering, including the Royal Society Summer Science Exhibition, Science Day at Buckingham Palace, and giving the Isambard Kingdom Brunel Award Lecture at the British Science Festival.

Affiliation: University of Edinburgh
Homepage: https://eng.ed.ac.uk/about/people/professor-timothy-drysdale

Lecturer: Jutta Kretzberg, Katja Hellekes
Fields: Personal / professional development

Content

Are you a student? Have you ever considered doing a PhD? Or a career in academia?
Does the idea of doing a PhD appeal to you? Or does it seem like hard work, or even a painful experience?
Many Master’s students struggle with the decision of whether a PhD would be the right choice for their career. In fact, a significant proportion of PhD students continue to question their decision until they graduate, and sometimes even afterwards.
There is no general advice on who should pursue a PhD. Whether to pursue a PhD is a personal decision that depends on factors such as your personality, personal situation, and the job opportunities available. The aim of this workshop is to help you develop a clearer personal perspective on this decision.

This workshop is primarily aimed at Master’s and advanced Bachelor’s students. However, the method of developing your personal perspective can also be applied to future career steps. PhD students, PhD holders and non-PhDs who are willing to share their perspectives are highly welcome!

Session 1: Background information
In the first session, we will begin by providing some background information on undertaking a PhD in Germany or Austria. What are the motivations for pursuing a PhD? What skills are gained through a PhD? How can a PhD be structured and funded? How do PhDs differ between disciplines and countries?

Session 2: External perspectives
In the second session, we will explore the different stakeholders’ perspectives interactively. What do Master’s students expect from a PhD? What do PhD supervisors expect from their students? What do employers expect from PhD versus Master’s degree applicants? What about the perspective of family and friends? And which personality traits might be useful for pursuing a PhD?

Session 3: Your personal perspective
During the third session, you will write down your hopes, neutral expectations and fears relating to a PhD. Working with a fellow participant, categorize these into the groups: ‘tasks/skills’, ‘topics/scientific questions’, ‘working environment’ and ‘personal factors’. Sharing your thoughts and listening to those of your teammate can help you gain a clearer perspective on your career decisions.

Session 4: How to become a PhD candidate?
After sharing our conclusions from the previous sessions, we will discuss the practical steps involved in becoming a PhD candidate, such as: How do you choose a topic? How do you find a project and a supervisor? How can you finance the PhD? We will also consider how to balance the demands of your PhD with your personal life – bridging realities of your live and a PhD.

Literature

• The European Competence Framework for Researchers: https://research-and-innovation.ec.europa.eu/document/download/7da29338-37bf-4d51-b5eb-a1571b84c7ad_en?filename=ec_rtd_research-competence-presentation.pdf
• General information on PhD scholarships (by German Government): https://www.bmbf.de/EN/Research/ScienceSystem/AcademicCareers/DoctoralScholarships/doctoralscholarships_node.html
• General information on German academic system & funding for international exchange (DAAD): https://www.daad.de/en/
• Largest scholarship organisation in Germany: https://www.studienstiftung.de/en/doctoral-scholarships/doctoral-scholarships
• Chris Woolston: “Graduate survey: A love-hurt relationship” Nature 550, 549-552 (2017)
• https://www.nature.com/articles/nj7677-549a https://doi.org/10.1038/nj7677-549a (Nature’s survey of more than 5,700 doctoral students worldwide)
• Lars Kiewidt, PhD: “To PhD or not to PhD?” (2019) https://medium.com/age-of-awareness/to-phd-or-not-to-phd-4312cdb862c5 (Evaluation of this survey data set concerning PhD student’s motivation, skills and satisfaction across fields in natural sciences.)
• Chris Woolston: ‘I don’t want this kind of life’: graduate students question career options
• Nature 611, 413-416 (2022) doi: https://doi.org/10.1038/d41586-022-03586-8 (Newest version of nature’s PhD survey, but not open access)
• Katie Mitzelfelt, PhD: “To Be or Not To Be a PhD Candidate, That Is the Question” (Association for Women in Science Magazine, 2021): https://awis.org/to-phd-or-not-phd/ (Individual perspectives of 6 persons on their own decision to be or not to be a PhD.)
• Charlotte King_: “To PhD or not to PhD, that is the question…” https://www.postgrad.com/blog/to-phd-or-not-to-phd/ (Rather old, but still helpful blog post)

Lecturer

Jutta Kretzberg is professor for Computational Neuroscience and head of the MSc program Neuroscience at University of Oldenburg. She studied applied computer science and biology at University of Bielefeld, where she also did her PhD in Biology. After being a postdoc (and having a baby) in San Diego, California, she came back to Germany to be a junior professor and became a professor some years (and another baby) later. Nowadays, while juggling her family, teaching, research and administration duties, her favorite task is mentoring.

Affiliation: University of Oldenburg
Homepage: https://uol.de/en/neurosciences/compneuro

Katja Hellekes, is an experienced academic professional and Coordinator of the Vienna Doctoral School Cognition, Behavior and Neuroscience. She completed her diploma and doctorate at the University of Cologne, specializing in Neurobiology, followed by postdoctoral research at the Institute of Molecular Pathology (IMP) and the University of Freiburg. Alongside her role as Coordinator of the Doctoral Program, Katja Hellekes lectures in cognitive science. With a passion for fostering the growth of early-career researchers, she provides dedicated support to PhD candidates, guiding them through their doctoral journey and helping them transition into independent research roles.

Affiliation: University Vienna
Homepage: https://vds-cobene.univie.ac.at/

Lecturer: Jennifer Fewell
Fields: Biology; Collective Behavior

Content

This course will explore the social organization and collective behavior of social insects from a biological perspective. The social insects are models for coordination and cooperation across small to large scales. Their distributed communication systems have been used extensively as inspiration for applied questions in coordination and collective behavior, from supply chains to robotics and beyond. Is a social insect colony the original collective \”AI\”? – well probably not, but it will be a fun question to explore!

Lecturer

Jennifer Fewell is a President\’s Professor at Arizona State University, where she served as the founding Director of the Center for Social Dynamics and Complexity. She has served also as President of the Animal Behavior Society and the International Union for the Study of Social Insects. She studies social organization and division of labor in social insects, and mechanisms and evolution of social cooperation across a range of species. She received her MS and PhD from the University of Colorado.

Affiliation: Arizona State University

Lecturer: Stephane Baele
Fields: AI, Political Science, Social Theory, History

Content

This course proposes to hit, for 270 minutes, the “pause” button in the relentless, high-paced development of Artificial Intelligence, to critically think of the political underpinnings and implications of the technology.

Specifically, it seeks to highlight the profound connection between (any) technology and power in order to unpack the various ways through which progress in AI cannot be separated from social and political hierarchies, state power and its often violent contestations, individual freedoms, and the international system.

The three sessions of the course explore this issue by unearthing what we could imagine as the three layers of the power/technology relationship (as applied to AI), from the most superficial to the most fundamental. First, we reflect on the issue – and very idea of – AI (and other technologies) “dual-uses”, using the case of extremist and terrorist uses of AI as a case-study. Second, this allows us to problematise, from various historical perspectives, the strengthening or weakening of state power in the AI “revolution”. Finally, we leverage a couple of classic social theory frameworks to interrogate the very nature and position of technique in society and how AI merely represents the latest stage of a much larger current of modernity.

Session 1: The problem with AI “dual-uses”: AI terrorism and extremism.
Session 2: AI, technology, and state power: A historical perspective.
Session 3: Questioning technique, progress, and power at the age of the AI “revolution”.

Literature

• Baele S. (2026) Generative Artificial Intelligence. In Lakhani S., Macdonald S., Droogan J., Khalil L. (eds.) Routledge Handbook of Online Violent Extremism. London: Routledge.
• Baele S., Brace L. (2024) AI Extremism. Technologies, Tactics, Actors. Dublin: VOX-Pol.
• Giattino C., Mathieu E., Samborska V., Roser M. (2023) Artificial Intelligence, OurWorldInData.org.
• Ellul J. (964) The Technological Society. New York: Vintage Books.
• Headrick D. (2009) Technology: A World History. Oxford: Oxford University Press.
• Hegghammer T. (2021) Resistance Is Futile. The War on Terror Supercharged State Power. Foreign Affairs 100(5): pp.44-53.
• Rassler D., Veilleux-Lepage Y. (2024) The Paradox of Progress: How ‘Disruptive,’ ‘Dual-use,’ ‘Democratized,’ and ‘Diffused’ Technologies Shape Terrorist Innovation. Zeitschrift für Technikfolgenabschätzung in Theorie und Praxis 33(2): 22-28.
• Tilly C. (1990) Coercion, Capital, and European States, AD 990-1990. Cambridge, Mass.: Blackwell.

Lecturer

Stephane J. Baele is Professor of International Studies at UCLouvain, Belgium, and Honorary Associate Professor in Security and Political Violence at the University of Exeter, UK. He has written extensively on extremist and violent political actors\’ communications, using in-depth empirical analysis of cases ranging from Islamic State\’s propaganda to white supremacists\’ online forums to offer novel perspectives on radicalization, digital extremism, and linguistic and visual expressions and drivers of violence. Besides academia, he regularly supports counter-terrorism/extremism agencies in various ways and loves to run on wild trails.

Affiliation: UCLouvain

Lecturer: Christian Herff
Fields: Machine Learning, Signal Processing, Neural Data

Content

This course will cover the field of Brain-Computer Interfaces that decode speech directly from invasive neural activity.

• Session one will introduce different measures of invasive neurophysiology and highlight the state of the art in speech BCIs.
• Session two, we will look at intracranial data directly and realize a first decoding system.
• Session three is dedicated to open challenges and future directions.

Literature

• Silva, A. B., Littlejohn, K. T., Liu, J. R., Moses, D. A., & Chang, E. F. (2024). The speech neuroprosthesis. Nature Reviews Neuroscience, 25(7), 473-492.

Lecturer

Christian Herff is an Associate Professor in the Department of Neurosurgery, where he leads the research program on invasive brain–computer interfaces (BCIs). With a background in computer science, he bridges technical innovation and clinical application to drive some of Europe’s most advanced work in neural interfaces. His leadership in the field is reflected in his election to the Board of the BCI Society, where he represents invasive BCI research.

Affiliation: Maastricht University
Homepage: https://www.maastrichtuniversity.nl/ce-herff