Mourad Gharbi

About

Mourad Gharbi is a leading researcher in brain-computer interfaces (BCI) and neural signal processing, with a focus on translating thought into machine action. His most influential work, “EEG Based Brain Computer Interface for Controlling a Robot Arm Movement Through Thought” (2018, 103 citations), demonstrates a direct neural pathway for robotic control, enabling users to manipulate a robot arm using only EEG signals. This breakthrough has significant implications for assistive technology and neuroprosthetics. Gharbi has also made key contributions to the classification of imagined hand movements, developing efficient machine learning pipelines that distinguish left from right motor imagery. His 2016 study (19 citations) employs an RBF kernel Support Vector Machine (SVM) to achieve high-accuracy discrimination, while his 2017 work (9 citations) refines this approach by integrating joint Continuous Wavelet Transform and Principal Component Analysis (CWT_PCA) for enhanced feature extraction. Together, these studies establish Gharbi as a pioneer in real-time, non-invasive BCI systems, offering scalable solutions for individuals with motor impairments. His work bridges computational neuroscience and practical robotics, with a citation trajectory that underscores its growing impact on human-machine interaction.

Research Focus

Key Achievements

Top Papers

1. 1
   EEG Based Brain Computer Interface for Controlling a Robot Arm Movement Through Thought

   103 citations · 2018
2. 2
   EEG efficient classification of imagined hand movement using RBF kernel SVM

   19 citations · 2016
3. 3
   EEG efficient classification of imagined right and left hand movement using RBF kernel SVM and the joint CWT_PCA

   9 citations · 2017

Key Collaborators

Contact & Links