Yazan M. Dweiri

Jordan University of Science and Technology

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About

Yazan M. Dweiri is a researcher whose work lies at the intersection of neural engineering, signal processing, and machine learning, with a focus on advancing prosthetic control systems. His key research areas include peripheral nerve signal extraction, Bayesian algorithms, and neuroevolution-based classification. Dweiri’s major contribution is the development of a model-based Bayesian signal extraction algorithm for peripheral nerves, which enables the decoding of fascicular-level motor commands from multi-channel cuff electrodes—a critical step toward providing intuitive, volitional control over robotic prostheses for amputees. This work, published in 2017, has garnered 21 citations, underscoring its impact on neural interface research. More recently, Dweiri has explored neuroevolution models for electromyography (EMG)-based hand gesture classification, a 2023 study with 9 citations that demonstrates his adaptability in applying evolutionary computation to biomedical signal analysis. His innovative approaches bridge theoretical modeling and practical application, offering promising pathways for restoring motor function. Dweiri’s contributions are particularly notable for their potential to transform human-machine interaction, making him a rising figure in neuroprosthetics and computational neuroscience.