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About

Henrich Kolkhorst is a pioneering researcher at the intersection of brain-computer interfaces (BCIs) and human-robot interaction, with a focus on making robot control more intuitive and non-intrusive. His work explores how brain signals can be decoded to infer user intentions, preferences, and even perceived hazards—eliminating the need for screens or physical interfaces. Kolkhorst’s most cited paper, “Guess What I Attend: Interface-Free Object Selection Using Brain Signals” (2018, 8 citations), introduced a novel method for robots to identify user goals directly from neural activity, enabling seamless cooperation. His 2017 study on decoding perceived hazardousness from brain states (6 citations) advanced safe human-robot collaboration by allowing robots to adapt their actions based on users’ subconscious risk assessments. More recently, “Learning User Preferences for Trajectories from Brain Signals” (2022) extended this paradigm to personalized motion planning. Kolkhorst’s contributions are particularly notable for their emphasis on screen-free, robust BCIs, as demonstrated in his 2020 work on robotic object selection. With a growing citation impact, his research is shaping the future of assistive robotics and intuitive human-machine communication.