Mahdi Alqahtani

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About

Mahdi Alqahtani is a researcher specializing in biomedical signal processing and machine learning, with a focus on electromyography (EMG)-based human-machine interfaces. His work centers on decoding motor intent from forearm muscle activity, particularly for prehension movements—the coordinated hand and finger motions essential for grasping and manipulation. In his most-cited study, "EMG-Based Classification of Forearm Muscles in Prehension Movements: Performance Comparison of Machine Learning Algorithms" (2020), Alqahtani systematically evaluated multiple classifiers, including support vector machines and neural networks, to optimize real-time gesture recognition. This contribution advances the development of intuitive prosthetic control systems and assistive technologies for individuals with motor impairments. Although his citation count remains modest, his research addresses a critical bottleneck in myoelectric control: achieving robust classification across diverse movement patterns. Alqahtani’s work bridges computational modeling and clinical application, offering practical insights for designing more responsive, user-adaptive prosthetics. His comparative analysis of algorithms provides a foundational benchmark for future studies in non-invasive neural decoding, underscoring his role in refining the accuracy and reliability of EMG-driven interfaces.