Matteo De Luca
Papers
1
Total Citations
2
H-Index
1
About
Matteo De Luca is a rising researcher at the intersection of biomedical engineering and cognitive neuroscience, with a primary focus on mental workload assessment and human-robot interaction. His most cited work, "EEG-Based Stress Assessment During Robot Assisted Surgery," introduces an adaptive pipeline for processing electroencephalographic signals to evaluate cognitive strain during surgical robot training. This study stands out for its novel approach to feature extraction, selection, and classification, moving beyond traditional statistical methods to leverage machine learning for real-time stress detection. Although early in his career—with his top paper currently holding 2 citations—De Luca’s contributions address a critical gap in surgical training, where objective measures of mental workload can enhance both safety and skill acquisition. His work has the potential to transform how trainees are monitored in high-stakes environments, offering a data-driven pathway to personalized feedback and reduced cognitive overload. As the field of neuroergonomics expands, De Luca’s adaptive EEG methodology positions him as a promising voice in the development of intelligent, responsive training systems for robotic surgery.
Research Focus
Key Achievements
Top Papers
- 1