Fakhita Regragui

About

Fakhita Regragui is a leading researcher in brain-computer interfaces (BCI) and human-robot interaction (HRI), with a focus on translating neural signals into real-world control. Her most influential work, “EEG Based Brain Computer Interface for Controlling a Robot Arm Movement Through Thought” (103 citations), demonstrates a direct pathway from imagined motor activity to robotic actuation, a milestone in assistive technology. Regragui’s core contributions lie in the efficient classification of imagined hand movements using EEG signals. She pioneered the use of the Radial Basis Function (RBF) kernel Support Vector Machine (SVM), achieving high discrimination between left and right hand motor imagery—a fundamental challenge in BCI. Her 2016 paper on this topic (19 citations) and its extension with joint Continuous Wavelet Transform and Principal Component Analysis (CWT_PCA) in 2017 (9 citations) refined signal processing pipelines for better accuracy and lower computational cost. Beyond EEG, Regragui explored depth-based 3D dynamic gesture recognition using the Kinect sensor (4 citations), computing upper-body joint angles for intuitive HRI. Her work bridges non-invasive neural decoding and practical robotic control, offering scalable solutions for rehabilitation and human augmentation.

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
4. 4
   A Depth-based Approach for 3D Dynamic Gesture Recognition

   4 citations · 2015

Key Collaborators

Contact & Links