Laura Ferrero
Papers
6
Total Citations
75
H-Index
4
About
Laura Ferrero is a researcher specializing in brain-machine interfaces (BMIs), lower-limb robotic exoskeletons, and neural rehabilitation engineering, with a particular focus on restoring mobility for individuals with spinal cord injury (SCI) and lower-extremity motor impairments. Her work sits at a compelling intersection of neuroscience, robotics, and artificial intelligence, pushing the boundaries of how human intention can be translated into machine-assisted movement. Ferrero's most significant contributions involve developing and validating BMI systems that use motor imagery — the mental simulation of movement — to control wearable exoskeletons. Her 2021 paper integrating attention-based BMI control has accumulated 37 citations, while her 2024 investigation into deep learning-driven asynchronous exoskeleton control has already attracted 18 citations, reflecting growing interest in AI-powered neural interfaces. She has also pioneered efforts to improve BMI reliability through error-related potential detection and transfer learning approaches to overcome limited training data — critical practical challenges in the field. Beyond technical innovation, Ferrero demonstrates a strong commitment to patient-centered research, assessing usability, user experience, and clinical acceptance of these technologies among SCI patients. This dual focus on engineering excellence and real-world applicability makes her work particularly valuable for the next generation of rehabilitation robotics researchers.
Research Focus
Key Achievements
Top Papers
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