Fazia Sbargoud

University of Sciences and Technology Houari Boumediene

Papers

Total Citations

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About

Fazia Sbargoud is a leading researcher in bio-robotics and human-machine interaction, specializing in the fusion and classification of bio-signals for advanced prosthetic control. Her work centers on integrating electroencephalography (EEG) and electromyography (EMG) signals to overcome the limitations of single-modality approaches, such as artifacts and information gaps. Her most cited paper, "WPT-ANN and Belief Theory Based EEG/EMG Data Fusion for Movement Identification" (2019, 24 citations), introduces a novel framework combining wavelet packet transform, artificial neural networks, and belief theory to enhance movement identification accuracy. This work is foundational for developing more intuitive and reliable robotic hand control systems. Sbargoud also explores hybrid classification strategies for EMG signals, as seen in her 2021 paper, aiming to directly translate muscle activity into precise robotic commands. Her research has significant implications for assistive technologies, rehabilitation, and neuroprosthetics, bridging the gap between neural signals and mechanical action. With a growing citation impact, Sbargoud is establishing herself as a key innovator in the field of intelligent bio-signal processing.