Maurice Rekrut

German Research Centre for Artificial Intelligence

Papers

Total Citations

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About

Maurice Rekrut is a researcher at the forefront of silent speech Brain-Computer Interfaces (BCIs) and human-robot collaboration, with a focus on making these technologies more intuitive and accessible. His key research areas include EEG-based speech imagery decoding, BCI training optimization, and multimodal human-robot interaction. Rekrut’s major contribution lies in developing innovative training procedures that transfer knowledge from overt to silent speech, significantly reducing the mental and physical exhaustion typically associated with BCI data collection. His 2022 paper on this topic, with 7 citations, proposes a method to improve training efficiency by leveraging overt speech patterns, a breakthrough that could accelerate practical BCI applications for communication-impaired individuals. Additionally, Rekrut has pioneered the use of gamification in BCI training, as shown in his 2024 work (4 citations), which introduces a “game with a purpose” to enhance participant engagement during EEG recording for speech imagery. In the realm of robotics, his 2023 study (3 citations) addresses the pick-and-place problem in human-robot collaboration through a multimodal teach-in approach combining speech, gestures, and gaze. Rekrut’s work is notable for its interdisciplinary impact, bridging neuroscience, human-computer interaction, and robotics to create more natural, efficient, and user-friendly interfaces.