Riccardo Rigoni

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About

Riccardo Rigoni is a researcher at the intersection of biomedical engineering, cognitive neuroscience, and human-robot interaction, with a primary focus on the real-time assessment of mental workload and stress using electroencephalography (EEG). His most cited work, "EEG-Based Stress Assessment During Robot Assisted Surgery," introduces an adaptive signal-processing pipeline that integrates feature extraction, selection, and machine learning classification to evaluate cognitive load during surgical robot training. This contribution is notable for its departure from prior studies by acquiring EEG signals in ecologically valid, hands-on training environments rather than controlled lab settings, thereby enhancing the translational potential of neuroergonomic monitoring. Rigoni’s research aims to improve surgical safety and training outcomes by enabling objective, non-invasive stress detection. Though his publication record is early-stage, his work has already garnered citations from the growing fields of neuroergonomics and surgical robotics. By combining statistical rigor with machine learning, Rigoni is helping to pave the way for adaptive human-machine interfaces that respond to the operator’s cognitive state—a critical step toward safer, more intuitive robotic surgery systems.