Henrich Kolkhorst

brains, robots, running

brains, robots, running.

I am interested in decoding users' brain states to improve human–robot interaction. Specifically, I work on learning from non-invasive brain signals to predict user judgment and preferences. These predictions allow users to influence not only what the robot does but also how these actions are performed.

In my free time, I enjoy endurance sports : running, cycling, swimming—also combined in triathlons.


  • brain–machine interfaces
  • human–robot interaction
  • brain state decoding


  • PhD candidate in Computer Science

    University of Freiburg

  • MSc in Computer Science, with distinction, 2013

    Karlsruhe Institute of Technology

  • BSc in Computer Science, with distinction, 2011

    Karlsruhe Institute of Technology

Recent Posts

Race report: Running 100 kilometers in 6:54

So I ran 100 kilometers last weekend. Crazy idea?—I agree. Yet it also provided a new view on personal limits and mental …

How to pace in a 100km run?

This post describes my analysis of split times in 100km races that helped set realistic expectations and pacing strategies.

Recent Publications

A Robust Screen-Free Brain-Computer Interface for Robotic Object Selection

Brain signals represent a communication modality that can allow users of assistive robots to specify high-level goals, such as the …

Learning User Preferences for Trajectories from Brain Signals

Robot motions in the presence of humans should not only be feasible and safe, but also conform to human preferences. This, however, …

Heterogeneity of Event-Related Potentials in a Screen-Free Brain-Computer Interface

Interacting with the environment using a brain-computer interface involves mapping a decoded user command to a desired real-world …

Influence of User Tasks on EEG-Based Classification Performance in a Hazard Detection Paradigm

Attention-based brain-computer interface (BCI) paradigms offer a way to exert control, but also to provide insight into a user’s …

Mining Within-Trial Oscillatory Brain Dynamics to Address the Variability of Optimized Spatial Filters

Data-driven spatial filtering algorithms optimize scores, such as the contrast between two conditions to extract oscillatory brain …