Manu Srinath Halvagal

École Polytechnique Fédérale de Lausanne

Papers

Total Citations

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About

Manu Srinath Halvagal is a researcher at the intersection of cognitive neuroscience, brain-computer interfaces (BCI), and human-robot interaction. His work focuses on leveraging noninvasive EEG signals to infer subjective human preferences and improve autonomous decision-making in robotic systems. His most-cited paper, “Inferring subjective preferences on robot trajectories using EEG signals” (2019, 20 citations), demonstrates how error-related potentials (ErrPs)—neural deflections triggered when humans perceive mistakes—can be decoded to guide robot behavior without explicit commands. This contribution bridges cognitive information processing with real-time machine learning, offering a novel pathway for more intuitive human-robot collaboration. Halvagal’s research has implications for assistive technologies, adaptive automation, and neuroergonomics, where understanding implicit neural feedback can enhance system responsiveness. His work stands out for its practical integration of EEG-based cognitive states into autonomous agent learning, a step toward seamless human-machine symbiosis. With growing interest in passive BCI and preference learning, Halvagal’s studies are increasingly cited in robotics and neural engineering communities, marking him as an emerging voice in embodied cognitive science.