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About

Hessa Al-Junaid is a pioneering researcher at the intersection of robotics, biomimetics, and brain-computer interfaces (BCI). Her work focuses on enabling intuitive robotic control through neural signals, with key contributions in visual servoing, EEG-based learning, and dexterous manipulation. Her most cited paper, "ANN Based Robotic Arm Visual Servoing Nonlinear System" (2015, 7 citations), introduced an artificial neural network framework to model visual-kinematic relations for precise 6-DOF robot arm positioning using only image data. She further advanced the field with "Biomimetic Based EEG Learning for Robotics Complex Grasping" (2018, 5 citations), which explored how biological grasping principles can be translated to prosthetic and robotic applications. Her subsequent work on EEG feature extraction and pattern recognition (2018–2019, 3–3 citations) applied PCA and deep learning to decode brainwave patterns from specific finger movements, enabling more natural control of robotic systems through BCIs. Al-Junaid’s research bridges neuroscience and robotics, offering novel pathways for assistive technologies and human-machine interaction. Her cumulative work has laid foundational methods for non-invasive neural control of robotic manipulators, with potential applications in rehabilitation and advanced prosthetics.