Safri Nahela

Papers

Total Citations

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About

Safri Nahela is a rising researcher at the intersection of artificial intelligence, biomedical signal processing, and assistive robotics. Her work focuses on decoding human intent from physiological signals—primarily electroencephalography (EEG) and electromyography (EMG)—to enable intuitive control of prosthetic limbs and rehabilitation devices. In her highly cited 2021 paper, Nahela pioneered an AI-driven IoT framework that uses deep learning to classify hand movements from EEG brain signals, directly addressing the challenge of translating user intention into robotic prosthetic action for individuals with hand amputations. This work has garnered 5 citations and laid groundwork for more responsive neural interfaces. Building on this, her 2023 study systematically evaluated feature extraction techniques across machine learning methods for finger movement classification using dual Myo armbands, achieving 4 citations. By comparing approaches for decoding complex finger gestures from EMG signals, she provided a critical benchmark for exoskeleton and powered wheelchair applications. Nahela’s contributions are particularly notable for bridging deep learning with wearable sensor technology, advancing the practicality of non-invasive brain-computer interfaces. Her research continues to shape the development of more natural, real-time control systems for assistive technologies.