Chokri Mhiri

About

Chokri Mhiri is a researcher at the forefront of intelligent systems, with key contributions spanning brain-computer interfaces (BCI) and advanced robotic control. His work on electroencephalography (EEG) signals has led to a novel ensemble learning approach for classifying motor imagery (MI) tasks, enabling real-time control of assistive devices such as cursors, wheelchairs, and prosthetics through mere mental imagery. This highly cited 2021 paper (17 citations) demonstrates his impact on non-invasive neural decoding. In parallel, Mhiri has advanced adaptive control theory by developing an L₁ adaptive fractional control framework optimized via genetic algorithms, specifically tailored for complex polyarticulated robotic systems. This 2021 work (10 citations) addresses the challenge of robust, high-performance control in multi-jointed robots, integrating fractional calculus with adaptive filtering to enhance stability and precision. His research uniquely bridges the gap between human neural signal processing and autonomous robotic actuation, offering practical solutions for rehabilitation engineering and industrial automation. Through these contributions, Mhiri has established himself as a versatile innovator, pushing the boundaries of how machines can interpret human intent and execute complex physical tasks with adaptive intelligence.

Research Focus

Key Achievements

Top Papers

1. 1
   A Novel Ensemble Learning Approach for Classification of EEG Motor Imagery Signals

   17 citations · 2021
2. 2
   <a:math xmlns:a="http://www.w3.org/1998/Math/MathML" id="M1"> <a:msub> <a:mrow> <a:mi>L</a:mi> </a:mrow> <a:mrow> <a:mn>1</a:mn> </a:mrow> </a:msub> </a:math> Adaptive Fractional Control Optimized by Genetic Algorithms with Application to Polyarticulated Robotic Systems

   10 citations · 2021

Key Collaborators

Contact & Links