ETH Zurich

Research Assistant in Signal Processing and Optimization for Brain-Computer Interfaces in Virtual Reality

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Research Assistant in Signal Processing and Optimization for Brain-Computer Interfaces in Virtual Reality

The Sensing, Interaction & Perception Lab is looking for PhD students in Brain-Computer Interfaces for Virtual Reality at ETH Zurich. The project will place a heavy focus on signal processing, adaptive systems, neuromarkers of perception, modeling presence & sickness.

Brain-Computer Interfaces are the next frontier in interactive systems to enable users to efficiently and effectively accomplish their tasks in immersive environments, such as Virtual Reality or Augmented Reality. Simultaneously, efficient Brain-Computer Interfaces enable us to refine our understanding of processes in cognitive neuroscience.

Brain-Computer Interfaces (BCI) analyze users’ brain activity to enable them to interact, without muscle activity, with computer systems. Originally designed to assist people with severe motor disabilities, a new trend in immersive environments is the use of BCIs by a wider public. For example, through passive BCI systems, it is possible to transparently monitor users’ mental states to estimate their cognitive load or their level of engagement in a task.

From a system's perspective, Virtual Reality (VR) environments immerse users in computer-generated 3D environments, allowing them full control over the displayed environment, and thus evoking a sense of presence in the virtual space. Immersion and this sense of presence opens up new fields of application, ranging from training and education to social networking and entertainment. Given the growing interest of society and the investments of major industrial groups, VR holds much promise in superseding the interaction platforms in human-computer interaction that we use on a daily basis, such as desktop computers, laptops tablets, phones, and wearables.

In this project, we will design and study novel VR systems that analyze the electrophysiological activity of a user’s brain, through a passive BCI, in order to improve the perception of virtual environments. The objective is to integrate into the VR system new ways to assess the user’s mental state through real-time classification of EEG data, determining the neural correlates of physiological markers and users' perceptions. The ultimate goal will be to improve user immersion by increasing levels of embodiment and reducing or preventing cybersickness through real-time adaptation of virtual content to mental states.

Project background

To date, a significant challenge in Virtual Reality systems is in reaching wide-spread adoption for everyday computing tasks. Rather, VR systems remain at the level of entertaining devices at this point, without harvesting their larger potential to replace mobile computing for everyday activities. A common explanation for this phenomenon is that current VR systems fail to characterize the user's mental state interaction and thus lack the means of adapting interactive environments accordingly. Related studies have shown that experiencing the same VR system and experiences evokes significantly varying responses in users—from a perception point as well as from the point of users' physiological reactions to immersive environments.

A major challenge of current systems is "cybersickness", a phenomenon that occurs in over 60% of users. The phenomenon refers to the set of deleterious symptoms that can occur after more or less prolonged use of virtual reality systems. Less severe, depending on personal characteristics, users can suffer from breaks in presence and immersion due to anomalies in rendering and interaction, which can lead to a bad feeling of embodiment in their virtual avatars. All these cases severely affect the user experience to the detriment of pleasant interactions and thus success of immersive computing. Therefore, research on these phenomena is required to tailor interaction to users' physiological reactions and adapt immersive environments in real-time.

Job description

The aim of this project is to develop intelligent and interactive software experiences that detect physiological reactions related to cybersickness/simulator sickness, breaks in presence, and embodiment. We will develop sensing models and signal processing algorithms for the early detection of these reactions. These will be input to our optimization-based engine for real-time system and UI adaptation.

The work will focus on the identification, characterization, and real-time detection of neurophysiological markers associated with users’ mental states. A second component of the work will focus on the real-time adaptation of the immersive environment. The work will comprise methodological and theoretical aspects. First, we will investigate the state of the art of the various dimensions associated with "user experience" in VR, the factors that influence their perception, and a range of “offline” markers that have been highlighted in prior work. From this, we will define experimental protocols for controlled empirical studies that we will conduct with state-of-the-art EEG headsets in immersive settings, where we will electroencephalographic data associated with different mental states of VR users. Through an in-depth analysis, we will determine the individual neuromarkers that relate to the various dimensions of user experience, with a particular focus on perception of presence, embodiment, and cybersickness.

For this data analysis, we will use and further develop signal processing techniques, including statistical methods as well as novel learning-based approaches. These include specific methods, based for example on Riemannian geometry tools. We aim at developing novel objective metrics that are capable of evaluating the evolution of feelings related to embodiment, presence, and sickness in real-time. We will compare our objective methods' success with the current default evaluation, which is mainly based on subjective questionnaires or on averaged experimental data.

In the second part of this project, we will integrate our detection of neuromarkers into immersive and interactive systems that operate in real-time. We will develop adaptive mechanisms for UI optimization following the physiological reactions our system observes in users. We will evaluate a series of strategies and mechanisms that re-engage users in immersive experiences or to reinforce their feeling of embodiment according to the results obtained by our quantification model. We will evaluate our systems end-to-end in empirical settings with representative participants.

We offer

We offer an exciting and stimulating environment to study and work in. For an overview of our other projects, learn more about Christian Holz and the Sensing, Interaction & Perception Lab (meet the team).

ETH has several internationally recognized research groups dedicated to signal processing, neurophysiological computing, machine learning, and physiological computing. We also collaborate with several other institutions and companies in Switzerland and abroad.

This project will contribute to a larger ambition in the domain of neurophysiological VR systems, where we are part of a collaboration with the CRIStAL lab at University of Lille, INRIA Rennes, Koç University, and University of Essex and the following people:

Your profile

  • written and spoken fluency in English
  • an excellent master's degree (M.Sc., M.Eng. or equivalent) in Computer Science, Electrical Engineering, Bioengineering, or a related field
  • solid signal processing skills (e.g., processing electrophysiological signals such as EEG, EMG, EOG)
  • programming experience, preferably in Python, working with signals or ML tools (e.g., pandas, MATLAB, TensorFlow, PyTorch)
  • experience implementing research prototypes (frontend, backend, or both)
  • strong interpersonal and communication skills

Ideal candidates should have worked with EEG headsets and data before, including being part or having conducted data acquisitions in controlled experiments, gaining experience with EEG apparatuses, verifying signal quality, and making sense of recordings.

Prior experience in conducting controlled user studies and analyzing collected EEG data is highly appreciated. Experience in developing immersive applications for AR/VR is a plus (e.g., Unity/C#). Background in cognitive sciences and/or neurophysiology is optional.

ETH Zurich

ETH Zurich is one of the world’s leading universities specialising in science and technology. We are renowned for our excellent education, cutting-edge fundamental research and direct transfer of new knowledge into society. Over 30,000 people from more than 120 countries find our university to be a place that promotes independent thinking and an environment that inspires excellence. Located in the heart of Europe, yet forging connections all over the world, we work together to develop solutions for the global challenges of today and tomorrow.


If you are interested in joining our group, please submit your complete application through the online application portal, including:

  • a (short) motivation letter
  • curriculum vitae
  • an overview of your prior experiences with EEG and BCI (especially experience with EEG headsets, acquisitions, studies)
  • school and university score records
  • contact details of at least two academic referees
  • a link to your Github profile and/or your personal portfolio/website

Applications will be evaluated on a rolling basis.

In line with our values, ETH Zurich encourages an inclusive culture. We promote equality of opportunity, value diversity and nurture a working and learning environment in which the rights and dignity of all our staff and students are respected. Visit our Equal Opportunities and Diversity website to find out how we ensure a fair and open environment that allows everyone to grow and flourish.


Research Assistant in Signal Processing and Optimization for Brain-Computer Interfaces in Virtual Reality
Rämistrasse 101 Zürich, Schweiz
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