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About

Michael Tangermann is a leading researcher at the intersection of brain-computer interfaces (BCIs) and human-robot interaction. His work focuses on enabling intuitive, non-intrusive communication between humans and machines by decoding brain signals to infer user intentions, preferences, and perceived hazards. Tangermann’s major contributions include developing screen-free BCIs that allow users to select objects for assistive robots simply by attending to them, as demonstrated in his 2018 paper "Guess What I Attend" (8 citations). He has also pioneered methods for decoding perceived hazardousness from brain states to shape safer human-robot collaboration (2017, 6 citations). His foundational work on BCIs and visual activity (2013, 5 citations) established key principles for translating brain states into actionable commands, such as selecting letters or moving robotic arms. More recently, Tangermann has explored learning user preferences for robot trajectories from brain signals (2022, 2 citations), pushing toward more personalized and adaptive robotic systems. With a cumulative citation count exceeding 25 across his most-cited works, Tangermann’s research is shaping the future of assistive robotics and intuitive human-machine interfaces, offering promising pathways for physically disabled individuals and collaborative robotics.