Rihab Bousseta

About

Rihab Bousseta is a pioneering researcher in the field of brain-computer interfaces (BCIs), with a focused expertise in electroencephalography (EEG)-based systems for motor imagery classification. Her work centers on enabling direct neural control of external devices, most notably demonstrated in her highly cited 2018 study (103 citations), which successfully translated imagined hand movements into real-time commands for a robot arm. This breakthrough showcases the practical potential of BCIs for assistive technology and neurorehabilitation. Bousseta’s core contributions lie in advancing EEG signal classification techniques. She has developed robust machine learning frameworks, particularly using Support Vector Machines (SVM) with Radial Basis Function (RBF) kernels, to discriminate between left and right imagined hand movements with high efficiency. Her 2016 paper (19 citations) established a foundational SVM-based method, while her 2017 work (9 citations) refined this approach by integrating joint Continuous Wavelet Transform (CWT) and Principal Component Analysis (PCA) for enhanced feature extraction and dimensionality reduction. With a total of over 130 citations across her key publications, Bousseta’s research has significantly impacted the BCI community, offering scalable, computationally efficient solutions for decoding motor intent. Her work bridges the gap between theoretical signal processing and practical neuroprosthetic control, making her a notable figure in the quest for intuitive, non-invasive brain-machine interfaces.

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