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Gentili, Rodolphe

Rodolphe Gentili
Assistant Professor
Department of Kinesiology
Program in Neuroscience and Cognitive Science
School of Public Health
2144 School of Public Health

The central theme of Dr. Gentili's research is to understand the brain processes underlying human motor behavior by employing, experimental cognitive-motor neuroscience, computational modeling and robotics-based approaches. Dr. Gentili uses electroencephalography (EEG), functional near-infrared spectroscopy (fNIRS), kinematics, dynamics, electromyography (EMG), computational modeling and robotics to examine the brain processes underlying human cognitive-motor adaptation, learning and performance.  The long-term goals of his research team include: i) understanding how the brain integrates the physical properties of upper-limb effectors and novel environments in relationship with specific cognitive processes (e.g., mental imagery, inhibitory, attentional mechanisms) during adaptive cognitive-motor behavior and ii) developing intelligent systems to monitor and enhance/regain cognitive-motor behavior through human-machine interaction. In addition his work will inform the development of the next generation of biomedical applications (e.g., brain biomarker monitoring, intervention programs, human-machine collaborative autonomy for rehabilitation). 

General Research Interests: 
  • Neuromorphic Engineering and Sensory-Motor Integration
  • Network Models 
  • Pattern Recognition

Dr. Gentili's research focuses on the investigation of functional non-invasive brain biomarkers, which assess the level of cognitive-motor performance and learning when humans interact with new dynamics or kinematics tools. Another aspect of his research is to develop bio-inspired control systems able to learn to manipulate anthropomorphic robot limbs (arm/finger), while at the same time incorporating the main biomechanical features of human movement. These two research fields contribute to the development of next generation smart prosthetics.

In order to better understand human motor control and learning processes, we employ an inclusive strategy involving cognitive-motor neuroscience, biomechanical analyses, and computational neuroscience that incorporates both experimental and modeling approaches. Our experimental work utilizes brain imaging and behavioral techniques to investigate the role of internal representations and cognitive-motor processes during human motor performance and learning. The modeling efforts include neurophysiologically plausible architectures, which mimic brain structures and/or functions, as well as biomechanics of effectors. The models are then tested both in computer simulations and by conducting "robotic experimentation" with actual anthropomorphic robot effectors, to i) validate the behavior of the neurophysiological model in the real world by comparing its performance to the human counterpart and ii) to examine human cognitive-motor processes in a context of human-robot team dynamics.

The experimental and computational approaches are complementary and enable to have an integrative perspective on human adaptive cognitive-motor behavior. The empirical work provides the basis on which to build and refine the computational models. In turn, the computational modeling effort is used as a tool to inform the development of empirical hypotheses as well as public health applications.

PhD from University of Burgundy, Dijon, France

Expertise in combining experimental and computational approaches that include behavioral analyses, neuroimaging, neural modelling, robotics and human-machine interactions to understand the cognitive-motor control and learning mechanisms of human movements.